WO2021114618A1

WO2021114618A1 - Federated learning method and apparatus, computer device, and readable storage medium

Info

Publication number: WO2021114618A1
Application number: PCT/CN2020/098890
Authority: WO
Inventors: 周学立; 朱恩东; 张茜; 刘丽扬
Original assignee: 平安科技（深圳）有限公司
Priority date: 2020-05-14
Filing date: 2020-06-29
Publication date: 2021-06-17
Also published as: CN111695674B; CN111695674A

Abstract

Provided are a federated learning method and apparatus, a computer device, and a readable storage medium. The federated learning method comprises: acquiring sample data and an identification code of the sample data; converting the sample data into a vector to obtain a vector representation of the sample data; encoding the vector representation to obtain an eigenvector of the sample data; and transmitting the eigenvector and the identification code to a data requesting end, such that the data requesting end looks up a label of the sample data on the basis of the identification code and implements federated learning model training on the basis of the eigenvector and the label. The present method increases the security of data during the process of federated learning.

Description

Federal learning method, device, computer equipment and readable storage medium

This application claims to be submitted to the Chinese Patent Office on May 14, 2020. The application number is 202010408557.4. The application titled "Federal learning methods, devices, computer equipment and readable storage media" is the priority of the Chinese patent application, the entire content of which is incorporated by reference Incorporated in this application.

Technical field

This application relates to the field of artificial intelligence technology, in particular to a federated learning method, device, computer equipment, and computer-readable storage medium.

Background technique

With the development of artificial intelligence technology, machine learning modeling by uniting different participants (participants, or parties, also known as data owners or clients) has become a development trend, that is, federated learning.

In federated learning, the inventor realized how to ensure data security while avoiding data distortion has become a problem to be solved.

Summary of the invention

In view of the above content, it is necessary to propose a federated learning method, device, computer equipment, and computer-readable storage medium, which can improve the efficiency of training federated learning models.

The first aspect of this application provides a federated learning method, and the federated learning method includes:

Acquiring sample data and an identification code of the sample data;

Converting the sample data into a vector to obtain a vector representation of the sample data;

Encoding the vector representation to obtain the feature vector of the sample data;

The feature vector and the identification code are transmitted to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs federated learning model training according to the feature vector and the label.

A second aspect of the present application provides a federated learning device, the federated learning device includes:

An obtaining module, used to obtain sample data and an identification code of the sample data;

A conversion module, configured to convert the sample data into a vector to obtain a vector representation of the sample data;

An encoding module, configured to encode the vector representation to obtain the feature vector of the sample data;

The transmission module is configured to transmit the feature vector and the identification code to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs processing according to the feature vector and the label Federation learning model training.

A third aspect of the present application provides a computer device that includes a processor, and the processor is configured to execute computer-readable instructions stored in a memory to implement the following steps:

Acquiring sample data and an identification code of the sample data;

A fourth aspect of the present application provides a computer-readable storage medium having computer-readable instructions stored on the computer-readable storage medium, and when the computer-readable instructions are executed by a processor, the following steps are implemented:

Acquiring sample data and an identification code of the sample data;

This application uses the coding model to perform feature learning and feature integration on the vector representation. Without a decoder, the data requesting end cannot interpret and obtain the sample data corresponding to the vector representation, which ensures the security of the data. The data requesting end does not directly obtain the data of the data providing end, which improves the security of the data in the federated learning process.

Description of the drawings

Fig. 1 is a flowchart of a federated learning method provided by an embodiment of the application.

Figure 2 is a structural diagram of a federated learning device provided by an embodiment of the present application.

Fig. 3 is a schematic diagram of a computer device provided by an embodiment of the present application.

Detailed ways

In order to be able to understand the above objectives, features and advantages of the application more clearly, the application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the application and the features in the embodiments can be combined with each other if there is no conflict.

In the following description, many specific details are set forth in order to fully understand the present application. The described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of this application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of this application. The terms used in the specification of the application herein are only for the purpose of describing specific embodiments, and are not intended to limit the application.

Preferably, the federated learning method of this application is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculation and/or information processing in accordance with pre-set or stored instructions. Its hardware includes, but is not limited to, a microprocessor and an application specific integrated circuit (ASIC) , Programmable Gate Array (Field-Programmable Gate Array, FPGA), Digital Processor (Digital Signal Processor, DSP), embedded equipment, etc.

This application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, small computers, large computers, including Distributed computing environment for any of the above systems or equipment, etc. This application may be described in the general context of computer-executable instructions executed by a computer, such as a program module. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments. In these distributed computing environments, tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

The computer device may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. The computer device can interact with the user through a keyboard, a mouse, a remote control, a touch panel, or a voice control device.

Example one

Fig. 1 is a flow chart of the federated learning method provided in Embodiment 1 of the present application. The federated learning method is applied to a data provider, and the data provider is a computer device for generating a federated learning model through federated learning.

The federated learning method specifically includes the following steps. According to different needs, the order of the steps in the flowchart can be changed, and some can be omitted.

101. Acquire sample data and an identification code of the sample data.

The identification code is the unique identification information of the sample data, and is used to identify the sample data between the data provider and the third party requesting the data.

The sample data may include different data types such as text data, one-hot data, numerical data, and embedding data.

For example, the data provider may be a financial company, the third party requesting the data may be an insurance company, the sample data may be user behavior data of the financial company, and the data type of the sample data may be text data. The identification code may be the user's mobile phone number or ID number corresponding to the user behavior data. The insurance company needs to obtain the coded user behavior data of the financial company, and use the insurance reliability scoring model to score the reliability of the user based on the obtained coded user behavior data. That is, insurance companies do not need to directly obtain user behavior data of financial companies, which protects the data security of financial companies. The insurance reliability scoring model can be a specific federated learning model, and the local federated learning model of an insurance company can be a deep learning model.

For another example, the data provider may be an e-commerce company, the third party requesting the data may be an advertising company, the sample data may be product click behavior data of the e-commerce company, and the data type of the sample data may be numeric data . The identification code may be the user's mobile phone number or ID number corresponding to the commodity click behavior data. The advertising company needs to obtain the coded product click behavior data of the e-commerce company, and recommend products to users according to the obtained coded product click behavior data through the product recommendation model. That is, the advertising company does not need to directly obtain the product click behavior data of the e-commerce company, which protects the data security of the e-commerce company. The product recommendation model can be a specific federated learning model, and the local federated learning model of the advertising company can be a deep learning model.

102. Convert the sample data into a vector to obtain a vector representation of the sample data.

In a specific embodiment, the converting the sample data into a vector includes:

Acquiring the data type of the sample data;

Judging whether the sample data needs to be converted into a vector according to the data type of the sample data;

If it is determined according to the data type of the sample data that the sample data needs to be converted into a vector, the sample data is converted into a vector according to a preset conversion method corresponding to the data type of the sample data.

For example, if the data type of the sample data is text data (the preset conversion method corresponding to the text data is the word2vec method), the sample data is converted into a vector according to the word2vec method. For another example, if the data type of the sample data is numeric data (the preset conversion method corresponding to the numeric data is a standardization method), the sample data is converted into a vector according to the standardization method.

Further, the judging whether the sample data needs to be converted into a vector according to the data type of the sample data includes:

Obtain a table of data types to be converted;

If the data type of the sample data exists in the data type table to be converted, the sample data needs to be converted into a vector;

If the data type of the sample data does not exist in the data type table to be converted, the sample data does not need to be converted into a vector. At this time, the data to be converted is a vector, and no conversion is required.

103. Encode the vector representation to obtain a feature vector of the sample data.

In a specific embodiment, the encoding the vector representation includes:

Get sample vector;

Training an encoding model through a backpropagation algorithm according to the sample vector, the encoding model consisting of an encoder and a decoder;

The vector representation is encoded with the trained encoder.

In order to ensure the security of the sample data, the data provider cannot directly request the sample data from a third party.

In a specific embodiment, the trained encoding model is optimized through the Deep auto-encoder (deep encoding) or sparse auto-encoder (sparse encoding) method.

The trained coding model may be optimized by a sparse coding method according to the difference between the output of the trained coding model and the input. The sparse coding method mainly optimizes the trained coding model by adding sparsity restriction conditions to the neural units in the trained coding model.

Specifically, the sparsity restriction condition may include, when the output value of the neuron is close to 1 (for example, greater than 0.9), the neuron is activated; when the output value of the neuron is close to 0 (for example, less than or equal to 0.9), the neuron is not activated.

The coding model is used to perform feature learning and feature integration on the vector representation. Without a decoder, the data requesting end cannot interpret and obtain the sample data corresponding to the vector representation, which ensures the security of the data.

104. Transmit the feature vector and the identification code to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs a federated learning model based on the feature vector and the label training.

The federated learning model includes: LR, XGB, DNN, etc. LR, XGB, DNN and other models are used for machine learning training and algorithm models for business use purposes. The federated learning model may be a specific artificial intelligence model, such as an artificial intelligence classification model, an artificial intelligence recognition model, and the like.

In a specific embodiment, the transmitting the feature vector and the identification code to the data requesting terminal includes:

The feature vector and the identification code are transmitted to the data requesting end through an encryption algorithm.

For example, the feature vector and the identification code are encrypted by the private key of the data provider; the encrypted feature vector and the identification code are transmitted to the data requesting terminal, so that the data requesting terminal passes through the data provider's The public key decrypts the encrypted feature vector and identification code.

When the sample data is user behavior data, the labels of the sample data are "risk user" and "normal user". When the sample data is product click behavior data, the labels of the sample data are "recommended product one", "recommended product two", and so on.

In a specific embodiment, the training of the federated learning model according to the feature vector and the label includes:

The data requesting terminal obtains the initial parameters of the federated learning model from a preset server;

The data requesting terminal initializes the federated learning model with the initial parameters;

The data requesting terminal locally trains the initialized federated learning model according to the feature vector and the label, updates the parameters of the initialized federated learning model, and obtains the updated parameters;

The data requesting end uploads the updated parameters to the preset server, so that the preset server performs aggregation processing on the parameters uploaded by each requesting end to obtain aggregation parameters, and when it is detected that the aggregation is used When the parameter-updated federated learning model is in a convergent state, deliver the updated federated learning model to the data requester;

The data requesting terminal receives the federated learning model issued by the preset server.

In another embodiment, before sending the updated federated learning model to the data requester, when the preset server detects that the federated learning model updated with the aggregation parameters is in a non-convergent state When the time, the preset server returns the aggregation parameter to the data requesting terminal, so that the data requesting terminal continues iterative training.

The federated learning method of the first embodiment generates a federated learning model through federated learning. The coding model is used to perform feature learning and feature integration on the vector representation, and the data requester cannot interpret and obtain sample data corresponding to the vector representation without a decoder, which ensures the security of the data To prevent data leakage. The coding model does not need to add noise to the vector representation, and avoids the generation of additional interference information due to the addition of noise. The establishment of the federation model has direct feedback on the coding results, which is beneficial to optimization and adjustment. The coding model can adjust the information loss degree and the information security degree of the feature learning and feature integration of the vector representation, find the compromise point of information security and information loss, and obtain more optimized parameters of the entire federated learning model. The data requesting end does not directly obtain the data of the data providing end, which improves the security of the data in the federated learning process.

In a specific embodiment, the federated learning method further includes:

Adjust the hyperparameters of the encoder and/or the federated learning model. Hyperparameters include network structure, number of neural layers, number of neurons in each layer, activation function, learning rate, regularization and penalty coefficients, loss function, and so on. Specifically, when the loss function floats and does not converge, the loss function, learning rate, and/or network structure can be adjusted. When the gradient disappears or the gradient explodes, adjust the activation function.

In a specific embodiment, the federated learning method further includes:

Acquiring the parameters of the trained federated learning model from the data requesting terminal;

Obtain the data to be processed;

Update the local federated learning model with the parameters of the trained federated learning model;

The updated local federated learning model is used to process the to-be-processed data.

In a specific embodiment, the federated learning method further includes:

Acquiring the processing result of the to-be-processed data;

Obtaining the preset result of the to-be-processed data;

The parameters and/or hyperparameters of the coding model and/or the federated learning model are adjusted according to the processing result of the to-be-processed data and the preset result of the to-be-processed data.

Specifically, it can be determined whether the coding model over-encodes data according to the processing result of the data to be processed and the preset result of the data to be processed. Over-encoding data may cause the encoding model to lose its ability to extract effective features; The judgment result adjusts the coding model to improve the feature extraction capability of the coding model and balance the feature extraction capability with the data security achieved through coding.

Example two

Fig. 2 is a structural diagram of a federated learning device provided in the second embodiment of the present application. The federated learning device 20 is applied to a data provider, and the data provider is a computer device. The federated learning device 20 is used to generate federated learning models through federated learning.

As shown in FIG. 2, the federated learning device 20 may include an acquisition module 201, a conversion module 202, an encoding module 203, and a transmission module 204.

The obtaining module 201 is used to obtain sample data and an identification code of the sample data.

The conversion module 202 is configured to convert the sample data into a vector to obtain a vector representation of the sample data.

Acquiring the data type of the sample data;

Obtain a table of data types to be converted;

The encoding module 203 is configured to encode the vector representation to obtain the feature vector of the sample data.

In a specific embodiment, the encoding the vector representation includes:

Get sample vector;

The vector representation is encoded with the trained encoder.

In another embodiment, the federated learning device 20 further includes an optimization module for optimizing the trained encoding model through a Deep auto-encoder (deep encoding) or sparse auto-encoder (sparse encoding) method.

The transmission module 204 is configured to transmit the feature vector and the identification code to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and according to the feature vector and the label Perform federated learning model training.

The federated learning device 20 of the second embodiment generates a federated learning model through federated learning. The coding model is used to perform feature learning and feature integration on the vector representation, and the data requester cannot interpret and obtain sample data corresponding to the vector representation without a decoder, which ensures the security of the data To prevent data leakage. The coding model does not need to add noise to the vector representation, and avoids the generation of additional interference information due to the addition of noise. The establishment of the federation model has direct feedback on the coding results, which is beneficial to optimization and adjustment. The coding model can adjust the information loss degree and the information security degree of the feature learning and feature integration of the vector representation, find the compromise point of information security and information loss, and obtain more optimized parameters of the entire federated learning model. The data requesting end does not directly obtain the data of the data providing end, which improves the security of the data in the federated learning process.

In a specific embodiment, the federated learning device 20 further includes an adjustment module for adjusting the hyperparameters of the encoder and/or the federated learning model. Hyperparameters include network structure, number of neural layers, number of neurons in each layer, activation function, learning rate, regularization and penalty coefficients, loss function, and so on. Specifically, when the loss function floats and does not converge, the loss function, learning rate, and/or network structure can be adjusted. When the gradient disappears or the gradient explodes, adjust the activation function.

In a specific embodiment, the federated learning device 20 further includes a processing module for obtaining the parameters of the trained federated learning model from the data requesting terminal; acquiring the data to be processed; using the trained federated learning model Update the local federated learning model with the parameters of, and process the data to be processed with the updated local federated learning model.

In a specific embodiment, the adjustment module is further configured to obtain the processing result of the to-be-processed data; obtain the preset result of the to-be-processed data; according to the processing result of the to-be-processed data and the to-be-processed data The preset result of adjusting the parameters and/or hyperparameters of the coding model and/or the federated learning model.

Example three

This embodiment provides a computer-readable storage medium having computer-readable instructions stored thereon. The computer-readable storage medium may be non-volatile or volatile. When the computer-readable instruction is executed by the processor, the steps in the above-mentioned federated learning method embodiment are implemented, for example, steps 101-104 shown in Fig. 1:

101. Obtain sample data and an identification code of the sample data;

Or, when the computer-readable instruction is executed by the processor, the function of each module in the above-mentioned device embodiment is realized, for example, the modules 201-204 in Fig. 2:

The obtaining module 201 is used to obtain sample data and an identification code of the sample data;

The conversion module 202 is configured to convert the sample data into a vector to obtain a vector representation of the sample data;

The encoding module 203 is configured to encode the vector representation to obtain the feature vector of the sample data;

Example four

FIG. 3 is a schematic diagram of the computer equipment provided in the fourth embodiment of the application. The computer device 30 includes a memory 301, a processor 302, and computer-readable instructions 303 stored in the memory 301 and running on the processor 302, such as a federated learning program. The processor 302 implements the steps in the embodiment of the federated learning method when the computer readable instruction 303 is executed, for example, steps 101-104 shown in FIG. 1:

101. Obtain sample data and an identification code of the sample data;

Exemplarily, the computer-readable instruction 303 may be divided into one or more modules, and the one or more modules are stored in the memory 301 and executed by the processor 302 to complete the method. . The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer readable instruction 303 in the computer device 30. For example, the computer-readable instruction 303 can be divided into the acquisition module 201, the conversion module 202, the encoding module 203, and the transmission module 204 in FIG. 2. For the specific functions of each module, refer to the second embodiment.

The computer device 30 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server. Those skilled in the art can understand that the schematic diagram 3 is only an example of the computer device 30, and does not constitute a limitation on the computer device 30. It may include more or less components than those shown in the figure, or combine certain components, or different components. For example, the computer device 30 may also include input and output devices, network access devices, buses, and so on.

The so-called processor 302 may be a central processing unit (Central Processing Unit, CPU), other general processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 302 can also be any conventional processor, etc. The processor 302 is the control center of the computer device 30, which uses various interfaces and lines to connect the entire computer device 30. Various parts.

The memory 301 may be used to store the computer-readable instructions 303, and the processor 302 executes or executes the computer-readable instructions or modules stored in the memory 301 and calls the data stored in the memory 301 to implement Various functions of the computer device 30. The memory 301 may mainly include a storage program area and a storage data area, where the storage program area may store an operating system, an application program required by at least one function (such as a sound playback function, an image playback function, etc.); the storage data area may Data and the like created in accordance with the use of the computer device 30 are stored. In addition, the memory 301 may include a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a Secure Digital (SD) card, a flash memory card (Flash Card), at least one disk storage device, flash memory Devices, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), or other non-volatile/volatile storage devices.

If the integrated module of the computer device 30 is implemented in the form of a software function module and sold or used as an independent product, it may be stored in a computer-readable storage medium. The computer-readable storage medium may be non-volatile or volatile. Based on this understanding, this application implements all or part of the processes in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. Here, when the computer-readable instruction is executed by the processor, it can implement the steps of the foregoing method embodiments. Wherein, the computer-readable instructions may be in the form of source code, object code, executable file, or some intermediate forms, etc. The computer-readable storage medium may include: any entity or device capable of carrying the computer-readable instructions, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, read only memory (ROM), random access memory ( RAM).

In the several embodiments provided in this application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the objectives of the solutions of the embodiments.

In addition, the functional modules in the various embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware, or in the form of hardware plus software functional modules.

The above-mentioned integrated modules implemented in the form of software functional modules may be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium and includes several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor execute the federated learning described in each embodiment of this application. Part of the method.

For those skilled in the art, it is obvious that the present application is not limited to the details of the foregoing exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of this application is defined by the appended claims rather than the above description, and therefore it is intended to fall into the claims. All changes in the meaning and scope of the equivalent elements of are included in this application. Any associated diagram marks in the claims should not be regarded as limiting the claims involved. In addition, it is obvious that the word "including" does not exclude other modules or steps, and the singular does not exclude the plural. Multiple modules or devices stated in the system claims can also be implemented by one module or device through software or hardware. Words such as first and second are used to denote names, but do not denote any specific order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit them. Although the application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the application can be Make modifications or equivalent replacements without departing from the spirit and scope of the technical solution of the present application.

Claims

A federated learning method, the federated learning method includes:

Acquiring sample data and an identification code of the sample data;

Converting the sample data into a vector to obtain a vector representation of the sample data;

Encoding the vector representation to obtain the feature vector of the sample data;

The feature vector and the identification code are transmitted to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs federated learning model training according to the feature vector and the label.
The federated learning method according to claim 1, wherein said converting said sample data into a vector comprises:

Acquiring the data type of the sample data;

Judging whether the sample data needs to be converted into a vector according to the data type of the sample data;

If it is determined according to the data type of the sample data that the sample data needs to be converted into a vector, the sample data is converted into a vector according to a preset conversion method corresponding to the data type of the sample data.
5. The federated learning method according to claim 1, wherein said encoding said vector representation comprises:

Get sample vector;

Training an encoding model through a backpropagation algorithm according to the sample vector, the encoding model consisting of an encoder and a decoder;

The vector representation is encoded with the trained encoder.
The federated learning method according to claim 1, wherein said transmitting said feature vector and said identification code to a data requesting terminal comprises:

The feature vector and the identification code are transmitted to the data requesting end through an encryption algorithm.
The federated learning method according to claim 1, wherein the training of the federated learning model according to the feature vector and the label comprises:

The data requesting terminal obtains the initial parameters of the federated learning model from a preset server;

The data requesting terminal initializes the federated learning model with the initial parameters;

The data requesting terminal locally trains the initialized federated learning model according to the feature vector and the label, updates the parameters of the initialized federated learning model, and obtains the updated parameters;

The data requesting end uploads the updated parameters to the preset server, so that the preset server performs aggregation processing on the parameters uploaded by each requesting end to obtain aggregation parameters, and when it is detected that the aggregation is used When the parameter-updated federated learning model is in a convergent state, deliver the updated federated learning model to the data requester;

The data requesting terminal receives the federated learning model issued by the preset server.
5. The federated learning method according to any one of claims 1 to 5, wherein the federated learning method further comprises:

The sparse coding method is used to optimize the trained coding model.
5. The federated learning method according to any one of claims 1 to 5, wherein the federated learning method further comprises:

Acquiring the processing result of the to-be-processed data;

Obtaining the preset result of the to-be-processed data;

The parameters and/or hyperparameters of the coding model and/or the federated learning model are adjusted according to the processing result of the to-be-processed data and the preset result of the to-be-processed data.
A federated learning device, wherein the federated learning device includes:

An obtaining module, used to obtain sample data and an identification code of the sample data;

A conversion module, configured to convert the sample data into a vector to obtain a vector representation of the sample data;

An encoding module, configured to encode the vector representation to obtain the feature vector of the sample data;

The transmission module is configured to transmit the feature vector and the identification code to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs processing according to the feature vector and the label Federation learning model training.
A computer device, wherein the computer device includes a processor, and the processor is configured to execute computer-readable instructions stored in a memory to implement the following steps:

Acquiring sample data and an identification code of the sample data;

Converting the sample data into a vector to obtain a vector representation of the sample data;

Encoding the vector representation to obtain the feature vector of the sample data;

The feature vector and the identification code are transmitted to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs federated learning model training according to the feature vector and the label.
9. The computer device according to claim 9, wherein when the processor executes the computer-readable instructions stored in the memory to implement the conversion of the sample data into a vector, the method comprises:

Acquiring the data type of the sample data;

Judging whether the sample data needs to be converted into a vector according to the data type of the sample data;

If it is determined according to the data type of the sample data that the sample data needs to be converted into a vector, the sample data is converted into a vector according to a preset conversion method corresponding to the data type of the sample data.
9. The computer device according to claim 9, wherein, when the processor executes the computer-readable instructions stored in the memory to implement the encoding of the vector representation, it comprises:

Get sample vector;

Training an encoding model through a backpropagation algorithm according to the sample vector, the encoding model consisting of an encoder and a decoder;

The vector representation is encoded with the trained encoder.
9. The computer device according to claim 9, wherein, when the processor executes the computer-readable instructions stored in the memory to implement the transmission of the feature vector and the identification code to the data requesting terminal, comprising:

The feature vector and the identification code are transmitted to the data requesting end through an encryption algorithm.
9. The computer device according to claim 9, wherein when the processor executes the computer-readable instructions stored in the memory to implement the training of the federated learning model according to the feature vector and the label, it comprises:

The data requesting terminal obtains the initial parameters of the federated learning model from a preset server;

The data requesting terminal initializes the federated learning model with the initial parameters;

The data requesting terminal locally trains the initialized federated learning model according to the feature vector and the label, updates the parameters of the initialized federated learning model, and obtains the updated parameters;

The data requesting end uploads the updated parameters to the preset server, so that the preset server performs aggregation processing on the parameters uploaded by each requesting end to obtain aggregation parameters, and when it is detected that the aggregation is used When the parameter-updated federated learning model is in a convergent state, deliver the updated federated learning model to the data requester;

The data requesting terminal receives the federated learning model issued by the preset server.
The computer device according to any one of claims 9-13, wherein the processor executing the computer-readable instructions stored in the memory is further configured to implement the following steps:

The sparse coding method is used to optimize the trained coding model.
The computer device according to any one of claims 9-13, wherein the processor executing the computer-readable instructions stored in the memory is further configured to implement the following steps:

Acquiring the processing result of the to-be-processed data;

Obtaining the preset result of the to-be-processed data;

The parameters and/or hyperparameters of the coding model and/or the federated learning model are adjusted according to the processing result of the to-be-processed data and the preset result of the to-be-processed data.
A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions implement the following steps when executed by a processor:

Acquiring sample data and an identification code of the sample data;

Converting the sample data into a vector to obtain a vector representation of the sample data;

Encoding the vector representation to obtain the feature vector of the sample data;

The feature vector and the identification code are transmitted to the data requesting terminal, so that the data requesting terminal searches for the label of the sample data according to the identification code, and performs federated learning model training according to the feature vector and the label.
The storage medium according to claim 16, wherein, when the computer-readable instructions are executed by the processor to implement the conversion of the sample data into a vector, it comprises:

Acquiring the data type of the sample data;

Judging whether the sample data needs to be converted into a vector according to the data type of the sample data;

If it is determined according to the data type of the sample data that the sample data needs to be converted into a vector, the sample data is converted into a vector according to a preset conversion method corresponding to the data type of the sample data.
15. The storage medium of claim 16, wherein, when the computer-readable instructions are executed by the processor to implement the encoding of the vector representation, the instructions include:

Get sample vector;

Training an encoding model through a backpropagation algorithm according to the sample vector, the encoding model consisting of an encoder and a decoder;

The vector representation is encoded with the trained encoder.
15. The storage medium according to claim 16, wherein, when the computer-readable instructions are executed by the processor to implement the transmission of the feature vector and the identification code to the data requesting terminal, comprising:

The feature vector and the identification code are transmitted to the data requesting end through an encryption algorithm.
15. The storage medium of claim 16, wherein, when the computer-readable instructions are executed by the processor to implement the training of the federated learning model according to the feature vector and the label, it comprises:

The data requesting terminal obtains the initial parameters of the federated learning model from a preset server;

The data requesting terminal initializes the federated learning model with the initial parameters;

The data requesting terminal locally trains the initialized federated learning model according to the feature vector and the label, updates the parameters of the initialized federated learning model, and obtains the updated parameters;

The data requesting end uploads the updated parameters to the preset server, so that the preset server performs aggregation processing on the parameters uploaded by each requesting end to obtain aggregation parameters, and when it is detected that the aggregation is used When the parameter-updated federated learning model is in a convergent state, deliver the updated federated learning model to the data requester;

The data requesting terminal receives the federated learning model issued by the preset server.