WO2020125251A1 - Federated learning-based model parameter training method, device, apparatus, and medium - Google Patents

Abstract

A federated learning-based model parameter training method, a device, an apparatus, and a medium. The method comprises: sending a first encryption model parameter to a second terminal (S100); receiving a first encryption loss value sent by the second terminal (S200), wherein the second terminal uses the first encryption model parameter as an initial parameter of a model to be trained, trains the model according to a second sample of the second terminal, and calculates the first encryption loss value, and a first sample and the second sample have the same feature dimension; decrypting the loss value, and detecting, according to the decrypted loss value, whether the model is in a convergence state (S300); and if so, using a second encryption model parameter determined on the basis of the loss value as a final parameter of the model (S400). The method realizes acquisition of a parameter in a model of a participant having incompletely labeled data by incorporating labeled sample data from the other participant in the two federated learning participants, thereby improving accuracy of the model of the participant having incompletely labeled data.

Description

Model parameter training method, device, equipment and medium based on federal learning The

This application requires the priority of the Chinese patent application submitted to the Chinese Patent Office on December 17, 2018, with the application number 201811547471.9 and the invention titled "Model Parameter Training Methods, Devices, Equipment, and Storage Media Based on Federal Learning," and all of its contents Incorporation by reference

Technical field

The present application relates to the field of data processing technology, and in particular to a model parameter training method, device, equipment, and medium based on federal learning.

Background technique

"Machine learning" is one of the core research areas of artificial intelligence, and how to continue machine learning under the premise of protecting data privacy and meeting legal compliance requirements is a trend that is now concerned in the field of machine learning. Under this background, people The study proposed the concept of "federal learning".

Federated learning uses technical algorithms to encrypt the built models. Both sides of the federation can conduct model training to obtain model parameters without giving their own data. Federated learning protects user data privacy through the exchange of parameters under the encryption mechanism. The data and the model itself It will not be transmitted, nor can it guess the other party’s data, so there is no possibility of leakage at the data level, nor does it violate stricter data protection laws such as GDPR (General Data Protection Regulation, "General Data Protection Regulation", etc., can maintain data integrity to a high degree while ensuring data privacy.

At present, in the case where the feature spaces of the A and B samples of the two federations are the same, the existing method based on federation modeling can only be based on the exchange of the parameters of the two parties for joint modeling when the A and B samples are marked. There are annotations, and the absence of annotations from Party B is not applicable. Therefore, how to combine the sample data of Party A to obtain the parameters in Party B model and improve the accuracy of Party B model is an urgent problem to be solved.

Summary of the invention

The main purpose of the present application is to provide a model parameter training method, device, equipment and medium based on federation learning, aiming to realize the union based on the feature space of the samples of the two federations being the same, if one side has a label and the other side has a missing label The sample data of the labeled party obtains the parameters in the model of the labeled missing party, which improves the accuracy of the model of the labeled missing party.

To achieve the above objective, the present application provides a model parameter training method based on federated learning, which is applied to the first terminal. The model parameter training method based on federated learning includes the following steps:

Sending a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

Receiving a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, and trains the second terminal according to a second sample of the second terminal The model to be trained, and the first encryption loss value is calculated; the first sample and the second sample have the same feature dimension;

Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.

Optionally, if it is detected that the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained includes:

If it is detected that the model to be trained is in a converged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

Updating the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

Sending the second encryption model parameter to the second terminal as the final parameter of the model to be trained.

Optionally, if it is detected that the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained includes:

If it is detected that the model to be trained is in a convergent state, a training stop instruction is sent to the second terminal, so that after receiving the training stop instruction, the second terminal uses the encryption corresponding to the loss value The gradient value updates the first encryption model parameter to obtain the second encryption model parameter, and uses the second encryption model parameter as the final parameter of the model to be trained.

Optionally, after the step of decrypting the loss value and detecting whether the model to be trained is in a converged state according to the decrypted loss value, the method further includes:

If it is detected that the model to be trained is in an unconverged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

Updating the first encryption model parameter according to the decrypted gradient value to obtain a third encryption model parameter;

Sending the third encryption model parameter to the second terminal, so that the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

Obtain the second encrypted loss value sent by the second terminal, and enter the step:

Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value.

Optionally, after the step of decrypting the loss value and detecting whether the model to be trained is in a converged state according to the decrypted loss value, the method further includes:

If it is detected that the model to be trained is in an unconverged state, a continuous training instruction is sent to the second terminal, so that after receiving the continuous training instruction, the second terminal according to the loss value The encryption gradient value updates the first encryption model parameter to obtain a third encryption model parameter, and the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

Obtain the second encrypted loss value sent by the second terminal, and enter the step:

Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value.

Optionally, if it is detected that the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained further includes:

Receiving the second encryption model parameter and the decryption request for the second encryption model parameter sent by the second terminal;

In response to the decryption request, decrypt the second encryption model parameter, and send the decrypted second encryption model parameter to the second terminal.

Optionally, if it is detected that the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained further includes:

Receiving an encryption prediction result obtained by the second terminal based on the second encryption model parameter and a decryption request for the encryption prediction result;

In response to the decryption request, decrypt the prediction result, and send the decrypted prediction result to the second terminal.

In addition, in order to achieve the above purpose, the present application also proposes a model parameter training device based on federal learning. The device is provided at the first terminal, and the device includes:

A first sending module, configured to send a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

A first receiving module, configured to receive a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, according to the second terminal Of the second sample to train the model to be trained and calculate the first encryption loss value; the first sample and the second sample have the same feature dimension;

A decryption detection module, used to decrypt the loss value and detect whether the model to be trained is in a converged state according to the decrypted loss value;

The determining module is configured to use the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained after the decryption detection module detects that the model to be trained is in a convergence state.

In addition, in order to achieve the above purpose, the present application also proposes a model parameter training device based on federation learning, the device includes: a memory, a processor, and a federation-based training device stored on the memory and capable of running on the processor The model parameter training readable instruction for learning, the model parameter training readable instruction based on federation learning implements the steps of the model parameter training method based on federation learning as described above when executed by the processor.

In addition, in order to achieve the above purpose, the present application also proposes a storage medium, which is applied to a computer, and the storage medium stores model parameter training readable instructions based on federal learning, and the model parameter training readable instructions based on federal learning When executed by the processor, the steps of the model parameter training method based on federation learning described above are realized.

In this application, the first encryption model parameter is sent to the second terminal, and the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal; An encryption loss value, wherein the second terminal uses the first encryption model parameter as the initial parameter of the model to be trained, trains the model to be trained according to the second sample of the second terminal, and calculates the A first encryption loss value; the first sample and the second sample have the same feature dimension; decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value; If it is detected that the model to be trained is in a convergent state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained; thus, in the case where the feature spaces of the samples of both federations are the same, The sample of the first terminal has a label, and when the sample label of the second terminal is missing, the model parameters of the second terminal are obtained by combining the sample data of the first terminal, and the accuracy of the model of the second terminal is improved.

BRIEF DESCRIPTION

FIG. 1 is a schematic structural diagram of a hardware operating environment involved in an embodiment of the present application;

2 is a schematic flowchart of a first embodiment of a model parameter training method based on federal learning in this application;

FIG. 3 is a schematic diagram of the detailed steps of step S400 in the first embodiment of the model parameter training method based on federal learning of this application;

4 is a schematic flowchart of a second embodiment of a model parameter training method based on federal learning in this application;

FIG. 5 is a schematic flowchart of a third embodiment of a model parameter training method based on federal learning in this application;

FIG. 6 is a schematic flowchart of a fourth embodiment of a model parameter training method based on federal learning in this application.

The implementation, functional characteristics and advantages of the present application will be further described in conjunction with the embodiments and with reference to the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

As shown in FIG. 1, FIG. 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application.

It should be noted that FIG. 1 is a schematic diagram of the hardware operating environment of the model parameter training device. The model parameter training device in the embodiment of the present application may be a terminal device such as a PC or a portable computer.

As shown in FIG. 1, the model parameter training device may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, and a communication bus 1002. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as disk storage. The memory 1005 may optionally be a storage device independent of the foregoing processor 1001.

Those skilled in the art can understand that the structure of the model parameter training device shown in FIG. 1 does not constitute a limitation on the model parameter training device, and may include more or fewer components than the illustration, or a combination of certain components, or different Parts layout.

As shown in FIG. 1, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and model parameter training readable instructions based on federation learning. Among them, the operating system is a readable instruction that manages and controls the hardware and software resources of the model parameter training device, and supports the operation of the model parameter training readable instruction based on federal learning and other software or readable instructions.

In the model parameter training device shown in FIG. 1, the user interface 1003 is mainly used for data communication with each terminal; the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; and the processor 1001 can be used for calling The model parameter training based on federated learning stored in the memory 1005 trains readable instructions, and performs the following operations:

Sending a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

Receiving a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, and trains the second terminal according to a second sample of the second terminal The model to be trained, and the first encryption loss value is calculated; the first sample and the second sample have the same feature dimension;

Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.

Based on the above structure, various embodiments of the model parameter training method based on federated learning are proposed.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of a first embodiment of a model parameter training method based on federal learning in this application.

The embodiment of the present application provides an embodiment of a model parameter training method based on federated learning. It should be noted that although the logic sequence is shown in the flowchart, in some cases, it may be executed in an order different from here The steps shown or described.

The model parameter training method based on federated learning in the embodiment of the present application is applied to the first terminal. The first terminal and the second terminal in the embodiment of the present application may be terminal devices such as PCs and portable computers, respectively, and are not specifically limited herein.

The model parameter training method based on federated learning in this embodiment includes:

Step S100: Send a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

"Machine learning" is one of the core research areas of artificial intelligence, and how to continue machine learning under the premise of protecting data privacy and meeting legal compliance requirements is a trend that is now concerned in the field of machine learning. Under this background, people The study proposed the concept of "federal learning".

Federated learning uses technical algorithms to encrypt the built models. Both sides of the federation can conduct model training to obtain model parameters without giving their own data. Federated learning protects user data privacy through the exchange of parameters under the encryption mechanism. The data and the model itself It will not be transmitted, nor can it guess the other party’s data, so there is no possibility of leakage at the data level, nor does it violate stricter data protection laws such as GDPR (General Data Protection Regulation, "General Data Protection Regulation", etc., can maintain data integrity to a high degree while ensuring data privacy.

At present, in the case where the feature spaces of the A and B samples of the two federations are the same, the existing method based on federation modeling can only be based on the exchange of the parameters of the two parties for joint modeling when the A and B samples are marked. There are annotations, and the absence of the B-side annotations is not applicable. To solve this problem, various embodiments of the model parameter training method based on federal learning in this application are proposed.

This application is based on horizontal federation learning. Horizontal federation learning refers to the overlapping of user features in two data sets (ie, the first sample and the second sample described in the embodiments of the present application), while the user overlap is less In the case of, divide the data set according to the horizontal direction (that is, the user dimension), and take out the part of the data with the same user characteristics but not the same users for training. This method is called horizontal federation learning. For example, there are two banks in different regions, and their user groups are from their respective regions, and their intersection is very small. However, their businesses are very similar, so the recorded user characteristics are the same.

In this embodiment, first the first terminal is trained according to the first sample of the first terminal to obtain the initial model parameters. The first terminal encrypts the initial model parameters using the encryption algorithm in the federated learning to obtain the first encryption model parameters, and the first An encryption model parameter is sent to the second terminal.

Step S200: Receive a first encryption loss value sent by the second terminal, where the second terminal uses the first encryption model parameter as the initial parameter of the model to be trained, based on the second sample of the second terminal Training the model to be trained, and calculating the first encryption loss value; the first sample and the second sample have the same feature dimension;

In this embodiment, after the second terminal receives the first encryption model parameters sent by the first terminal, the second terminal uses the first encryption model parameters as the initial parameters of the model to be trained, and according to the second terminal only The second sample data with a small amount of labels is used for model training, and the encryption loss value is calculated. It can be understood that the second sample label is partially missing compared to the first sample label, that is, the second sample label is higher than the first The label of a copy should be less.

The second terminal uses the first encryption model parameter as the initial parameter of its model to be trained, trains the model to be trained according to the second sample of the second terminal that is missing, and calculates the first encryption loss value Then, the first encrypted loss value is sent to the first terminal, and the first terminal receives the first encrypted loss value sent by the second terminal.

Step S300, decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

After receiving the encrypted loss value sent by the second terminal, the first terminal decrypts the encrypted loss value sent by the second terminal based on the corresponding decryption algorithm, and detects the to-be-trained according to the decrypted loss value Whether the model is converging.

Further, as an implementation manner, whether the model to be trained is in a converged state is detected according to the decrypted loss value, which may specifically be that the first terminal decrypts the encryption loss values sent by the second terminal twice in succession, Calculate the difference between these two loss values, and determine whether the difference is less than or equal to a preset threshold. When it is determined that the difference is less than or equal to the preset threshold, determine that the model to be trained is in a converged state When it is determined that the difference is greater than the preset threshold, it is determined that the model to be trained is not in a converged state.

Step S400, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.

During the process of training the model to be trained according to the parameters of the first encryption model, the second terminal calculates the corresponding encryption gradient value and encryption loss value. Due to the encryption of the first encryption model parameters, the second terminal cannot judge the pending value based on the encryption loss value. Whether the training model has converged, the second terminal sends the encrypted loss value to the first terminal, and the first terminal decrypts the loss value to determine whether the model to be trained has converged.

In this embodiment, the first terminal detects that the model to be trained is in a convergence state according to the decrypted loss value, and uses the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained. Training model training is complete.

Specifically, referring to FIG. 3, FIG. 3 is a schematic diagram of the refinement steps of step S400 in this embodiment; as an implementation manner, in this embodiment, step S400 may include the following refinement steps:

Step S401: If it is detected that the model to be trained is in a convergence state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

Step S402: Update the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

Step S403: Send the second encryption model parameters to the second terminal as the final parameters of the model to be trained.

As an implementation manner, the first terminal detects that the model to be trained is in a converged state, and the first terminal obtains the encrypted gradient value corresponding to the loss value sent by the second terminal and decrypts the gradient value. In the example, during the process of training the model to be trained according to the parameters of the first encryption model, the second terminal calculates the corresponding encryption gradient value and encryption loss value, and sends the calculated encryption gradient value and encryption loss value to the first terminal simultaneously , The first terminal first decrypts the encrypted loss value, then detects that the model to be trained is in a converged state according to the decrypted current loss value, and then the first terminal decrypts the encrypted gradient value corresponding to the current loss value, and then decrypts The gradient value of is used to update the first encryption model parameter to obtain the second encryption model parameter.

The first terminal sends the second encryption model parameters to the second terminal and determines the second encryption model parameters as the final parameters of the model to be trained of the second terminal, and the training of the model to be trained is completed. Therefore, when the feature spaces of the samples of the two federations are the same, the sample of the first terminal is labeled, and the sample of the second terminal is missing, the sample data of the first terminal is combined to obtain the model parameters of the second terminal. Improve the accuracy of the second terminal model.

Further, in other embodiments of this application, different from this embodiment, in step S400, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target The steps of training the final parameters of the model include the following refinement steps:

If it is detected that the model to be trained is in a convergent state, a training stop instruction is sent to the second terminal, so that after receiving the training stop instruction, the second terminal uses the encryption corresponding to the loss value The gradient value updates the first encryption model parameter to obtain the second encryption model parameter, and uses the second encryption model parameter as the final parameter of the model to be trained.

Different from the first embodiment of the model parameter training method based on federation learning, in this embodiment, the second terminal calculates the encryption gradient value and the encryption loss value during the process of training the model to be trained according to the first encryption model parameter, The second terminal only sends the calculated encrypted loss value to the first terminal, the first terminal decrypts the encrypted loss value, and detects that the model to be trained is in a convergence state according to the decrypted current loss value, and the first terminal sends Stop training instruction to the second terminal, after receiving the stop training instruction, the second terminal updates the first encryption model parameter according to the calculated encryption gradient value corresponding to the loss value to obtain the second Encrypt the model parameters, and use the second encrypted model parameters as the final parameters of the model to be trained, and the training of the model to be trained is completed, thereby realizing the case that the feature space of the samples of the two federations is the same, the sample of the first terminal If there is a label, and the sample label of the second terminal is missing, the sample data of the first terminal is combined with the model parameters of the second terminal to improve the accuracy of the model of the second terminal.

In this embodiment, the first encryption model parameter is sent to the second terminal, and the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal; The first encryption loss value, wherein the second terminal uses the first encryption model parameter as the initial parameter of the model to be trained, trains the model to be trained according to the second sample of the second terminal, and calculates The first encryption loss value; the first sample and the second sample have the same feature dimension; decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value ; If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained. It is realized that when the feature space of the samples of the two federations is the same, the sample of the first terminal is labeled, and the sample of the second terminal is missing, the sample data of the first terminal is combined to obtain the model parameters of the second terminal, and the second The accuracy of the terminal model.

Further, the second embodiment of the model parameter training method based on federal learning in this application is proposed.

Referring to FIG. 4, FIG. 4 is a schematic flowchart of a second embodiment of a model parameter training method based on federal learning according to the present application. Based on the first embodiment of the model parameter training method based on federal learning described above, in this embodiment, step S300 The loss value, and after the step of detecting whether the model to be trained is in a converged state according to the decrypted loss value, the method further includes:

Step S501: If it is detected that the model to be trained is in an unconverged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

The first terminal detects that the model to be trained is in an unconverged state, and the first terminal obtains the encrypted gradient value corresponding to the loss value sent by the second terminal and decrypts the gradient value. In this embodiment, the second During the process of training the model to be trained according to the parameters of the first encryption model, the terminal calculates the encryption gradient value and the encryption loss value, and simultaneously sends the calculated encryption gradient value and the encryption loss value to the first terminal, and the first terminal first decrypts the encryption The loss value, and then it is detected that the model to be trained is in an unconverged state according to the decrypted current loss value.

Step S502: Update the first encryption model parameter according to the decrypted gradient value to obtain a third encryption model parameter;

After detecting that the model to be trained is in an unconverged state, the first terminal decrypts the encryption gradient value corresponding to the current loss value, and updates the first encryption model parameters according to the decrypted gradient value to obtain the third encryption model parameter.

Step S503: Send the third encryption model parameter to the second terminal, so that the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

The first terminal sends the third encryption model parameter to the second terminal, and the second terminal continues to train the model to be trained according to the third encryption model parameter, and calculates the second encryption loss value and the encryption gradient corresponding to the second encryption loss value Value, the first terminal sends the second encryption loss value to the first terminal for the first terminal to detect whether the model to be trained has converged.

Step S504: Obtain the second encrypted loss value sent by the second terminal, and proceed to step S300, decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value; After obtaining the second encrypted loss value, the first terminal enters the first terminal to decrypt the loss value, and detects whether the model to be trained is in a converged state according to the decrypted loss value. The first terminal detects When the model to be trained is in a convergent state, step S400 is entered to determine that the second encrypted model parameter corresponding to the loss value in the current model convergence state is the final parameter of the model to be trained. The model training is completed, if the first terminal detects If the model to be trained is in an unconverged state, step S501 is entered again, and the second terminal continues to iteratively train the model to be trained according to the updated encryption model parameters and sends the encryption loss value calculated in the training process to the first terminal until After the first terminal detects that the model to be trained is in a convergence state according to the encryption loss value sent by it, the second terminal obtains the final encryption parameters of the model to be trained determined by the first terminal, and the model training of the second terminal is completed.

Further, as an implementation manner, in other embodiments of the present application, it is different from the second embodiment of the model parameter training method based on federal learning. In step S300, the loss value is decrypted, and according to the decrypted The step of detecting whether the loss value is in a convergence state after the loss value further includes:

If it is detected that the model to be trained is in an unconverged state, a continuous training instruction is sent to the second terminal, so that after receiving the continuous training instruction, the second terminal according to the loss value The encryption gradient value updates the first encryption model parameter to obtain a third encryption model parameter, and the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

Obtain the second encrypted loss value sent by the second terminal, and proceed to step S300, decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value.

It can be understood that, unlike the second embodiment of the model parameter training method based on federated learning, in this embodiment, the first terminal detects that the model to be trained is in an unconverged state, and then sends a continuous training instruction to all The second terminal, and the process of updating the encryption model parameters based on the encryption gradient value is performed at the second terminal. After the second terminal receives the continuation training instruction sent by the first terminal, the second terminal according to the loss value Update the first encryption model parameter with the corresponding encryption gradient value to obtain the third encryption model parameter, and then the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate the second encryption loss value, and Send the second encrypted loss value to the first terminal, and after obtaining the second encrypted loss value sent by the second terminal, the first terminal proceeds to step S300, that is, to enter the first terminal to decrypt the loss value, and according to the decrypted The step of detecting whether the model to be trained is in a convergent state by the loss value, and the first terminal detects that the model to be trained is in a convergent state, then proceeds to step S400 to determine a second encryption model corresponding to the loss value in the current convergent state The parameter is the final parameter of the model to be trained, and the model training is completed; if the first terminal detects that the model to be trained is in an unconverged state, it sends another training instruction to the second terminal to continue training and receives the second terminal to continue training The encryption loss value sent in the process until the first terminal detects that the model to be trained is in a convergence state based on the encryption loss value sent by the second terminal, the second terminal updates the final encryption parameter of the model to be trained according to the encryption gradient value, the second The training of the terminal to be trained is completed.

In this embodiment, in the above manner, when the feature spaces of the samples of the two federations are the same, the sample of the first terminal is labeled, and the sample of the second terminal is missing, the sample data of the first terminal is combined to obtain the second terminal Model parameters to improve the accuracy of the second terminal model.

Further, a third embodiment of the model parameter training method based on federal learning in this application is proposed.

Referring to FIG. 5, FIG. 5 is a schematic flowchart of a third embodiment of a model parameter training method based on federal learning according to the present application. Based on the first embodiment of the model parameter training method based on federal learning described above, in this embodiment, step S400, if detected When the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained further includes:

Step S601: Receive the second encryption model parameter and a decryption request for the second encryption model parameter sent by the second terminal;

Step S602, in response to the decryption request, decrypt the second encryption model parameter, and send the decrypted second encryption model parameter to the second terminal.

In the case where the feature space of the samples of both federations is the same, the samples of the first terminal are labeled, and the samples of the second terminal are missing, the sample data of the first terminal is combined to enable the second terminal to obtain the encrypted model parameters of the training. This implementation In an example, the first terminal receives the second encryption model parameter and the decryption request for the second encryption model parameter sent by the second terminal, and in response to the decryption request, decrypts the second encryption model parameter , And send the decrypted second encrypted model parameters to the second terminal, so that the second terminal can predict the results according to the decrypted model parameters, and realize the application of the model trained by the first terminal to the features And mark the missing second terminal, which greatly expands the scope of application of federal learning and effectively improves the predictive ability of the second terminal model.

Further, the fourth embodiment of the model parameter training method based on federal learning in this application is proposed.

Referring to FIG. 6, FIG. 6 is a schematic flowchart of a fourth embodiment of a model parameter training method based on federal learning according to the present application. Based on the first embodiment of the model parameter training method based on federal learning described above, in this embodiment, step S400, if detected When the model to be trained is in a convergence state, the step of using the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained further includes:

Step S603: Receive an encryption prediction result obtained by the second terminal based on the second encryption model parameter and a decryption request for the encryption prediction result;

Step S604, in response to the decryption request, decrypt the prediction result, and send the decrypted prediction result to the second terminal.

In this embodiment, when the feature space of the samples of the two federations is the same, the samples of the first terminal are labeled, and the sample labels of the second terminal are missing, the sample data of the first terminal is combined, and the second terminal obtains the trained encryption model Parameters, further, the first terminal receives an encryption prediction result obtained by the second terminal based on the second encryption model parameter and a decryption request for the encryption prediction result, and in response to the decryption request, decrypts the prediction As a result, the decrypted prediction result is sent to the second terminal, so that the second terminal can predict the result according to the finally determined encryption model parameters to obtain the encryption prediction result, and the first terminal will encrypt the prediction result After decryption, it is returned to the second terminal, and the model trained by the first terminal is applied to the second terminal with missing features and annotations, thereby greatly expanding the scope of application of federal learning and effectively improving the predictive ability of the second terminal model .

In addition, the embodiments of the present application also provide a model parameter training device based on federal learning. The device is provided at the first terminal, and the device includes:

A first sending module, configured to send a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

A first receiving module, configured to receive a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, according to the second terminal Of the second sample to train the model to be trained and calculate the first encryption loss value; the first sample and the second sample have the same feature dimension;

A decryption detection module, used to decrypt the loss value and detect whether the model to be trained is in a converged state according to the decrypted loss value;

The determining module is configured to use the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained after the decryption detection module detects that the model to be trained is in a convergence state.

Optionally, the determination module includes:

An acquisition and decryption unit for acquiring an encryption gradient value corresponding to the loss value sent by the second terminal and decrypting the gradient value after the decryption detection module detects that the model to be trained is in a convergence state;

An updating unit, configured to update the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

The first determining unit is configured to send the second encryption model parameter to the second terminal as the final parameter of the model to be trained.

Optionally, the determination module includes:

A second determining unit, configured to send a training stop instruction to the second terminal after the decryption detection module detects that the model to be trained is in a convergence state, so that the second terminal receives the training stop After the instruction, update the first encryption model parameter according to the encryption gradient value corresponding to the loss value to obtain the second encryption model parameter, and use the second encryption model parameter as the final parameter of the model to be trained .

Optionally, the device further includes:

Acquiring a decryption module, for acquiring an encrypted gradient value corresponding to the loss value sent by the second terminal and decrypting the gradient value after the decryption detection module detects that the model to be trained is in an unconverged state;

An update module, configured to update the first encryption model parameter according to the decrypted gradient value to obtain a third encryption model parameter;

A second sending module, configured to send the third encryption model parameter to the second terminal, so that the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

The first obtaining module is configured to obtain the second encrypted loss value sent by the second terminal, and send the second encrypted loss value to the decryption detection module.

Optionally, the device further includes:

A third sending module, configured to send a continuation training instruction to the second terminal after the decryption detection module detects that the model to be trained is in an unconverged state, so that the second terminal receives the continuation After the training instruction, the first encryption model parameter is updated according to the encryption gradient value corresponding to the loss value to obtain the third encryption model parameter, and the second terminal continues to train the third encryption model parameter according to the third encryption model parameter To train the model and calculate the second encryption loss value;

The second obtaining module is configured to obtain the second encrypted loss value sent by the second terminal, and send the second encrypted loss value to the decryption detection module.

Optionally, the device further includes:

A second receiving module, configured to receive the second encryption model parameter and the decryption request for the second encryption model parameter sent by the second terminal;

The first decryption module is configured to decrypt the second encryption model parameter in response to the decryption request, and send the decrypted second encryption model parameter to the second terminal.

Optionally, the device further includes:

A third receiving module, configured to receive an encrypted prediction result obtained by the second terminal based on the second encryption model parameters and a decryption request for the encrypted prediction result;

The second decryption module is configured to decrypt the prediction result in response to the decryption request, and send the decrypted prediction result to the second terminal.

The steps of the model parameter training device based on federation learning proposed in this embodiment implement the steps of the model parameter training method based on federation learning as described above, and will not be repeated here.

In addition, an embodiment of the present application also provides a model parameter training device based on federation learning, the device includes: a memory, a processor, and a model based on federation learning stored on the memory and operable on the processor Parameter training readable instruction, the model parameter training readable instruction based on federation learning implements the steps of the model parameter training method based on federation learning described above when executed by the processor.

For the method implemented when the model parameter training readable instruction based on federation learning running on the processor is executed, reference may be made to various embodiments of the model parameter training method based on federation learning in the present application, and details are not described here. The

In addition, an embodiment of the present application further proposes a computer-readable storage medium on which is stored a model parameter training readable instruction based on federal learning, and the model parameter training readable instruction based on federal learning is executed by a processor To implement the steps of the model parameter training method based on federated learning as described above.

For the method implemented when the model parameter training readable instruction based on federation learning running on the processor is executed, reference may be made to various embodiments of the model parameter training method based on federation learning in the present application, and details are not described here.

It should be noted that in this article, the terms "include", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, It also includes other elements that are not explicitly listed, or include elements inherent to this process, method, article, or device. Without more restrictions, the element defined by the sentence "include one..." does not exclude that there are other identical elements in the process, method, article or device that includes the element.

The sequence numbers of the above embodiments of the present application are for description only, and do not represent the advantages and disadvantages of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware, but in many cases the former is better Implementation. Based on this understanding, the technical solutions of the present application can essentially be reflected in the form of software products, and the computer software products are stored in a storage medium (such as ROM/RAM, magnetic disk, The CD-ROM includes several instructions to enable a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to perform the methods described in the embodiments of the present application.

The above are only preferred embodiments of the present application, and do not limit the scope of the patent of the present application. Any equivalent structure or equivalent process transformation made by the description and drawings of this application, or directly or indirectly used in other related technical fields The same reason is included in the patent protection scope of this application.

Claims (20)

1. A model parameter training method based on federation learning, which is applied to a first terminal, and the model parameter training method based on federation learning includes the following steps:

   Sending a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

   Receiving a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, and trains the second terminal according to a second sample of the second terminal The model to be trained, and the first encryption loss value is calculated; the first sample and the second sample have the same feature dimension;

   Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

   If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.
2. The model parameter training method based on federated learning according to claim 1, wherein, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target The steps to train the final parameters of the model include:

   If it is detected that the model to be trained is in a converged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

   Updating the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

   Sending the second encryption model parameter to the second terminal as the final parameter of the model to be trained.
3. The model parameter training method based on federated learning according to claim 1, wherein, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target The steps to train the final parameters of the model include:

   If it is detected that the model to be trained is in a convergent state, a training stop instruction is sent to the second terminal, so that after receiving the training stop instruction, the second terminal uses the encryption corresponding to the loss value The gradient value updates the first encryption model parameter to obtain the second encryption model parameter, and uses the second encryption model parameter as the final parameter of the model to be trained.
4. The model parameter training method based on federation learning according to claim 1, wherein the step of decrypting the loss value and detecting whether the model to be trained is in a converged state according to the decrypted loss value further comprises :

   If it is detected that the model to be trained is in an unconverged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

   Updating the first encryption model parameter according to the decrypted gradient value to obtain a third encryption model parameter;

   Sending the third encryption model parameter to the second terminal, so that the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

   Obtain the second encrypted loss value sent by the second terminal, and enter the step:

   Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value.
5. The method for training model parameters based on federated learning according to claim 1, characterized in that

   After the step of decrypting the loss value and detecting whether the model to be trained is in a converged state according to the decrypted loss value, the method further includes:

   If it is detected that the model to be trained is in an unconverged state, a continuous training instruction is sent to the second terminal, so that after receiving the continuous training instruction, the second terminal according to the loss value The encryption gradient value updates the first encryption model parameter to obtain a third encryption model parameter, and the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

   Obtain the second encrypted loss value sent by the second terminal, and enter the step:

   Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value.
6. The model parameter training method based on federated learning according to claim 1, wherein, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target After the final parameter step of training the model also includes:

   Receiving the second encryption model parameter and the decryption request for the second encryption model parameter sent by the second terminal;

   In response to the decryption request, decrypt the second encryption model parameter, and send the decrypted second encryption model parameter to the second terminal.
7. The model parameter training method based on federated learning according to claim 1, wherein, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target After the final parameter step of training the model also includes:

   Receiving an encryption prediction result obtained by the second terminal based on the second encryption model parameter and a decryption request for the encryption prediction result;

   In response to the decryption request, decrypt the prediction result, and send the decrypted prediction result to the second terminal.
8. A model parameter training device based on federal learning, wherein the device is provided at a first terminal, and the device includes:

   A first sending module, configured to send a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

   A first receiving module, configured to receive a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, according to the second terminal Of the second sample to train the model to be trained and calculate the first encryption loss value; the first sample and the second sample have the same feature dimension;

   A decryption detection module, used to decrypt the loss value and detect whether the model to be trained is in a converged state according to the decrypted loss value;

   The determining module is configured to use the second encryption model parameter determined based on the loss value as the final parameter of the model to be trained after the decryption detection module detects that the model to be trained is in a convergence state.
9. The model parameter training device based on federal learning according to claim 8, wherein the determination module comprises:

   An acquisition and decryption unit for acquiring an encryption gradient value corresponding to the loss value sent by the second terminal and decrypting the gradient value after the decryption detection module detects that the model to be trained is in a convergence state;

   An updating unit, configured to update the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

   The first determining unit is configured to send the second encryption model parameter to the second terminal as the final parameter of the model to be trained.
10. The model parameter training device based on federal learning according to claim 8, wherein the determination module comprises:

    A second determining unit, configured to send a training stop instruction to the second terminal after the decryption detection module detects that the model to be trained is in a convergence state, so that the second terminal receives the training stop After the instruction, update the first encryption model parameter according to the encryption gradient value corresponding to the loss value to obtain the second encryption model parameter, and use the second encryption model parameter as the final parameter of the model to be trained .
11. The apparatus for training model parameters based on federal learning according to claim 8, wherein the apparatus further comprises:

    Acquiring a decryption module, for acquiring an encrypted gradient value corresponding to the loss value sent by the second terminal and decrypting the gradient value after the decryption detection module detects that the model to be trained is in an unconverged state;

    An update module, configured to update the first encryption model parameter according to the decrypted gradient value to obtain a third encryption model parameter;

    A second sending module, configured to send the third encryption model parameter to the second terminal, so that the second terminal continues to train the model to be trained according to the third encryption model parameter and calculate a second encryption loss value;

    The first obtaining module is configured to obtain the second encrypted loss value sent by the second terminal, and send the second encrypted loss value to the decryption detection module.
12. The model parameter training device based on federal learning as claimed in claim 8, characterized in that

    Therefore, the device further includes:

    A third sending module, configured to send a continuation training instruction to the second terminal after the decryption detection module detects that the model to be trained is in an unconverged state, so that the second terminal receives the continuation After the training instruction, the first encryption model parameter is updated according to the encryption gradient value corresponding to the loss value to obtain the third encryption model parameter, and the second terminal continues to train the third encryption model parameter according to the third encryption model parameter To train the model and calculate the second encryption loss value;

    The second obtaining module is configured to obtain the second encrypted loss value sent by the second terminal, and send the second encrypted loss value to the decryption detection module.
13. The apparatus for training model parameters based on federal learning according to claim 8, wherein the apparatus further comprises:

    A second receiving module, configured to receive the second encryption model parameter and the decryption request for the second encryption model parameter sent by the second terminal;

    The first decryption module is configured to decrypt the second encryption model parameter in response to the decryption request, and send the decrypted second encryption model parameter to the second terminal.
14. The apparatus for training model parameters based on federal learning according to claim 8, wherein the apparatus further comprises:

    A third receiving module, configured to receive an encrypted prediction result obtained by the second terminal based on the second encryption model parameters and a decryption request for the encrypted prediction result;

    The second decryption module is configured to decrypt the prediction result in response to the decryption request, and send the decrypted prediction result to the second terminal.
15. A model parameter training device based on federated learning, wherein the device includes: a memory, a processor, and readable instructions for model parameter training based on federated learning stored on the memory and runable on the processor, When the readable instruction for model parameter training based on federation learning is executed by the processor, the following steps are implemented:

    Sending a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

    Receiving a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, and trains the second terminal according to a second sample of the second terminal The model to be trained, and the first encryption loss value is calculated; the first sample and the second sample have the same feature dimension;

    Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

    If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.
16. The model parameter training device based on federal learning according to claim 15, wherein, if it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the target The steps to train the final parameters of the model include:

    If it is detected that the model to be trained is in a converged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

    Updating the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

    Sending the second encryption model parameter to the second terminal as the final parameter of the model to be trained.
17. The model parameter training device based on federal learning according to claim 15, wherein, if it is detected that the model to be trained is in a converged state, the second encryption model parameter determined based on the loss value is used as the target The steps to train the final parameters of the model include:

    If it is detected that the model to be trained is in a convergent state, a training stop instruction is sent to the second terminal, so that after receiving the training stop instruction, the second terminal uses the encryption corresponding to the loss value The gradient value updates the first encryption model parameter to obtain the second encryption model parameter, and uses the second encryption model parameter as the final parameter of the model to be trained.
18. A storage medium, wherein it is applied to a computer, and the storage medium stores model parameter training readable instructions based on federated learning, and the model parameters training readable instructions based on federated learning implement the following steps when executed by a processor:

    Sending a first encryption model parameter to a second terminal, where the first encryption model parameter is obtained by training the first terminal according to the first sample of the first terminal;

    Receiving a first encryption loss value sent by the second terminal, wherein the second terminal uses the first encryption model parameter as an initial parameter of the model to be trained, and trains the second terminal according to a second sample of the second terminal The model to be trained, and the first encryption loss value is calculated; the first sample and the second sample have the same feature dimension;

    Decrypt the loss value, and detect whether the model to be trained is in a converged state according to the decrypted loss value;

    If it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained.
19. The storage medium according to claim 18, wherein if it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained The steps include:

    If it is detected that the model to be trained is in a converged state, obtain an encrypted gradient value corresponding to the loss value sent by the second terminal, and decrypt the gradient value;

    Updating the first encryption model parameter according to the decrypted gradient value to obtain a second encryption model parameter;

    Sending the second encryption model parameter to the second terminal as the final parameter of the model to be trained.
20. The storage medium according to claim 18, wherein if it is detected that the model to be trained is in a convergence state, the second encryption model parameter determined based on the loss value is used as the final parameter of the model to be trained The steps include:

    If it is detected that the model to be trained is in a convergent state, a training stop instruction is sent to the second terminal, so that after receiving the training stop instruction, the second terminal uses the encryption corresponding to the loss value The gradient value updates the first encryption model parameter to obtain the second encryption model parameter, and uses the second encryption model parameter as the final parameter of the model to be trained. The