WO2021008017A1 - Procédé d'apprentissage de fédération, système, dispositif terminal, et support d'informations - Google Patents

Procédé d'apprentissage de fédération, système, dispositif terminal, et support d'informations Download PDF

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Publication number
WO2021008017A1
WO2021008017A1 PCT/CN2019/116464 CN2019116464W WO2021008017A1 WO 2021008017 A1 WO2021008017 A1 WO 2021008017A1 CN 2019116464 W CN2019116464 W CN 2019116464W WO 2021008017 A1 WO2021008017 A1 WO 2021008017A1
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federated learning
alliance
enterprise
model
request
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PCT/CN2019/116464
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English (en)
Chinese (zh)
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程勇
刘洋
陈天健
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深圳前海微众银行股份有限公司
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Publication of WO2021008017A1 publication Critical patent/WO2021008017A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • This application relates to the technical field of financial technology (Fintech), in particular to a federated learning method, system, terminal device and storage medium.
  • Federated learning Learning refers to the combination of different participants (participant, or party, also known as data owner (data owner), or client (client)) for machine learning modeling methods.
  • participants do not need to ask other participants and coordinators (coordinator, also called server), parameter server (parameter server), or aggregation server (aggregation server)) expose the data you own, so federated learning can protect user privacy and ensure data security, and can solve the problem of data islands.
  • federated learning alliance always has an initiator And the participants who keep joining. In this way, a lot of manpower, material resources, and time resources need to be consumed in the implementation process of the enterprise's field construction of federated learning.
  • the main purpose of this application is to provide a federated learning method, system, terminal equipment and storage medium, aiming to save the enterprises wishing to conduct federated learning, and reduce the cost of manpower, material resources and time resources in the process of constructing federated learning model.
  • the cost of learning model construction is to provide a federated learning method, system, terminal equipment and storage medium, aiming to save the enterprises wishing to conduct federated learning, and reduce the cost of manpower, material resources and time resources in the process of constructing federated learning model.
  • this application provides a federated learning method, which includes the following steps:
  • the method further includes:
  • the step of forming a federal learning alliance according to the federal learning request includes:
  • the target enterprise is matched from the preset enterprise information database as alliance members to form a federated learning alliance.
  • the step of extracting member information of each alliance member in the federated learning alliance includes:
  • the step of constructing a federated learning model based on the member information includes:
  • the step of managing the federated learning model includes:
  • the steps of model training and model update of the federated learning model constructed by the management include:
  • the acquired federated learning request is a management request, and model training and model update management are performed on the federated learning model according to the management request; or,
  • the preset management strategy periodically perform model training and model update management on the federated learning model.
  • the federated learning method further includes:
  • the step of serving the alliance members of the federated learning alliance includes:
  • this application also provides a federal learning system, which includes:
  • the acquisition module is set to acquire the federated learning request received by the input interface
  • the alliance formation module is set to form a federal learning alliance according to the federal learning request
  • the detection module is configured to extract member information of each alliance member in the federated learning alliance
  • the model management module is configured to construct a federated learning model based on the member information and manage the federated learning model.
  • the federated learning system further includes:
  • the analysis module is configured to extract the requested items carried in the federated learning request and analyze whether the requested items can be executed.
  • the federated learning system further includes:
  • the service module is configured to provide services to the alliance members of the federated learning alliance according to the request items of the federated learning request.
  • this application also provides a terminal device.
  • the terminal device includes a memory, a processor, and computer-readable instructions that are stored on the memory and can run on the processor.
  • the computer-readable instructions are The processor implements the steps of the federated learning method as described above when executed.
  • the present application also provides a storage medium applied to a computer, and computer-readable instructions are stored on the storage medium, and when the computer-readable instructions are executed by a processor, the steps of the federated learning method described above are implemented.
  • This application obtains the federated learning request received by the input interface; forms a federated learning alliance according to the federated learning request; extracts member information of each alliance member in the federated learning alliance; constructs a federated learning model based on the member information, and Manage the federated learning model.
  • the suitable company is selected as the partner of the enterprise that submitted the federated learning request to form a federated learning alliance.
  • the member information carried by each member of the learning alliance is constructed into a federated learning model suitable for the federated learning of each alliance member, and the completed federated learning model is managed.
  • Figure 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application
  • Figure 2 is a schematic flow chart of the first embodiment of the federated learning method of this application.
  • step S300 in FIG. 2 is a schematic diagram of detailed steps of step S300 in FIG. 2;
  • FIG. 4 is a schematic diagram of the modules of the Federal Learning System of this application.
  • Fig. 1 is a schematic structural diagram of a hardware operating environment involved in a solution of an embodiment of the present application.
  • Fig. 1 can be a structural diagram of the hardware operating environment of the federated learning method device.
  • the device of the federated learning method in the embodiment of this application may be a terminal device such as a PC and a portable computer.
  • the federated learning method 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.
  • the communication bus 1002 is used to implement connection and communication between these components.
  • the user interface 1003 may include a display screen (Display) and an input unit such as a keyboard (Keyboard), and the optional user interface 1003 may also 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 can be a high-speed RAM memory or a stable memory (non-volatile memory), such as disk storage.
  • the memory 1005 may also be a storage device independent of the foregoing processor 1001.
  • the device structure of the federated learning method shown in FIG. 1 does not constitute a limitation on the device of the federated learning method, and may include more or fewer components than shown in the figure, or a combination of certain components, or different The layout of the components.
  • a memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and computer readable instructions.
  • the operating system is a program that manages and controls the hardware and software resources of the sample federated learning method equipment, and supports the operation of computer readable instructions and other software or programs.
  • the user interface 1003 is mainly used for data communication with various terminals;
  • the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server;
  • the processor 1001 can be used for calling Computer readable instructions stored in the memory 1005, and perform the following operations:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005, and after executing the step of obtaining the federated learning request received by the input interface, execute the following steps:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • the target enterprise is matched from the preset enterprise information database as alliance members to form a federated learning alliance.
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • the acquired federated learning request is a management request, and model training and model update management are performed on the federated learning model according to the management request; or,
  • the preset management strategy periodically perform model training and model update management on the federated learning model.
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • processor 1001 may also be used to call computer-readable instructions stored in the memory 1005 and execute the following steps:
  • FIG. 2 is a schematic flowchart of the first embodiment of the federal learning method of this application.
  • the embodiment of this application provides an embodiment of the federated learning method. It should be noted that although the logical sequence is shown in the flowchart, in some cases, the sequence shown or described may be executed in a different order than here. A step of.
  • the federated learning method of the embodiment of this application is applied to the device of the federated learning method.
  • the device of the federated learning method of the embodiment of this application may be a terminal device such as a PC and a portable computer, which is not specifically limited here.
  • Step S100 Obtain the federated learning request received by the input interface.
  • the federated learning request input by the enterprise user is obtained from the input interface set by the federated learning method device.
  • an enterprise user who wants to participate in federated learning can input the service request of the enterprise user who wants to participate in federated learning based on an input interface set on a PC, a portable computer or other federated learning method equipment.
  • a business user who wants to participate in federated learning is based on the input interface (specifically, the input window for service request) set up on the PC terminal (one of the federated learning method devices) for the corporate user to enter the service request, enter the input that he wants to participate in federated learning
  • the input interface specifically, the input window for service request
  • the PC terminal one of the federated learning method devices
  • step S100 after obtaining the federated learning request received by the input interface, the federated learning method of this application further includes:
  • Step S500 Extract request items carried in the federated learning request, and determine that the request items can be executed.
  • the federated learning service request input by an enterprise user may include information about the data owned by the enterprise user (for example, the amount of data and data characteristics of the user of the enterprise user), as well as information about the current enterprise user
  • the requested items of the federated learning model requested to participate for example, the problems that need to be solved through federated learning, the performance indicators of the requested federated learning model and other service requirements).
  • the federated learning service request Contains information about the data owned by the enterprise users themselves, as well as the service requirements that the enterprise users need to solve for the federated learning model requested to participate, and the performance indicators of the requested federated learning model, and further analyze the requirements of the enterprise users to pass
  • the problems solved by federated learning can be solved by constructing a federated learning model, and whether the constructed federated learning model can meet the performance indicators of the learning model requested by business users and so on.
  • Step S200 forming a federated learning alliance according to the federated learning request.
  • the cooperative enterprise of the corporate user who is currently initiating the federated learning request is searched for, thereby forming a federated learning alliance.
  • step S200 includes:
  • step S201 it is determined that the requested item can be executed, and then the enterprise label information carried in the federated learning request is detected.
  • the federated learning service request input by the enterprise user may also include the enterprise label information of the enterprise user who entered the federated learning service request (for example, enterprise attribute parameters such as the enterprise type and data type used to identify the enterprise user) ).
  • the analysis determines that the problems that enterprise users need to solve through federated learning can be solved by constructing a federated learning model, and the federated learning model constructed can also meet the performance indicators of the learning model requested by the corporate user, start to detect the corporate user
  • the enterprise label information in the input federated learning service request such as enterprise attribute parameters such as the enterprise type and data type of the enterprise user.
  • Step S202 According to the enterprise tag information, the target enterprise is matched from the preset enterprise information database as alliance members to form a federated learning alliance.
  • the corporate tag information of the corporate users carried in the detected federal learning request from all corporate users stored in the preset corporate information database, find and determine the target company suitable for establishing a cooperative relationship, so as to be an alliance member with the current Enterprise users who input the federal study request form a federal study alliance.
  • the preset enterprise information database is a database used to store the attribute parameters of the enterprise users who are willing to participate in federated learning (willing to participate in the federated learning) collected in advance, wherein the preset enterprise information database stores Each wants to participate in the federation to learn one or more attribute parameters of enterprise users, such as: the type of enterprise, the type of data owned by the enterprise, and the type of enterprise business, as well as the amount of data owned by the enterprise user, the characteristics of the data, the label of the data, and the ability of the enterprise data Completed machine learning tasks (for example, detection, prediction, or classification); the performance indicators of the federated learning model expected by each enterprise user, and the federated learning overhead that can be undertaken (for example, communication overhead, time overhead, computing resources, power overhead), hope
  • the federated learning benefits obtained for example, model performance improvement, or compensation).
  • the enterprise type, data type and other enterprise attribute parameters of the enterprise user who currently wants to participate in the federated learning are saved from the saved In the preset enterprise information database of one or more attribute parameters, one by one finds out target enterprises that are the same as or complementary to the enterprise type, data type and other enterprise attribute parameters of the enterprise user currently input to federated learning as the alliance member, and the current input Enterprise users of Federated Learning jointly formed the Federal Learning Alliance.
  • Step S300 Extract member information of each alliance member in the federated learning alliance.
  • the detected member information of each alliance member in the federated learning alliance includes but is not limited to: data information (for example: the amount of data owned by the enterprise, the characteristics of the data owned, and the label of the data), and the information of each enterprise user Hope based on the task information completed by federal learning (for example: face recognition, loan risk control, product recommendation, etc.).
  • data information for example: the amount of data owned by the enterprise, the characteristics of the data owned, and the label of the data
  • federal learning for example: face recognition, loan risk control, product recommendation, etc.
  • step S300 extracting member information of each alliance member in the federated learning alliance includes:
  • Step S301 detecting all the enterprise parameters stored in the preset enterprise information database.
  • all the enterprise parameters of all enterprise users who wish to participate in federated learning stored in the preset enterprise information database are detected, including: The attribute parameters of enterprise users, such as the type of enterprise, the type of data owned by the enterprise, and the type of enterprise business, as well as the amount of data owned by the enterprise user, the characteristics of the data, the label of the data, and the machine learning tasks that the enterprise data can complete (for example, detection , Prediction or classification); the performance indicators of the federated learning model expected by each enterprise user, the federated learning overhead that can be undertaken (for example, communication overhead, time overhead, computing resources, power overhead), and the expected federated learning benefits (for example, model Performance improvement rate, or cash reward).
  • the attribute parameters of enterprise users such as the type of enterprise, the type of data owned by the enterprise, and the type of enterprise business, as well as the amount of data owned by the enterprise user, the characteristics of the data, the label of the data, and the machine learning tasks that the enterprise data can complete (for example, detection , Prediction or classification); the performance indicators of
  • Step S302 Index the enterprise label information of the target enterprise corresponding to each alliance member from all the enterprise parameters.
  • each alliance member when it is detected that the current federated learning alliance formed based on the corporate label information (enterprise attribute parameters such as corporate type, data type, etc.) of corporate users carried in the federated learning request, each alliance member also saves corporate label information, All enterprise parameters of each enterprise user stored in the detected preset enterprise information database (each wish to participate in the federation to learn one or more attribute parameters of enterprise users, such as: enterprise type, enterprise data type and enterprise business type, and The amount of data owned by enterprise users, the characteristics of the data, the labels of the data, the machine learning tasks that enterprise data can complete, such as prediction or classification; the performance indicators of the federated learning model expected by each enterprise user, and the federated learning overhead that they can bear, For example, communication costs, time costs, computing resources, power costs, desired federated learning benefits, such as model performance improvement), traverse the enterprise parameters of enterprise users corresponding to each alliance member to index the current federated learning alliance , Enterprise label information of each alliance member (that is, one or more attribute parameters of enterprise users that
  • step S303 the member information of each federated member required to form the federated learning alliance is extracted from the enterprise label information one by one.
  • the detected label information of each alliance member that is, one or more attribute parameters and data information of each alliance member, For example: the amount of data owned by the enterprise, the characteristics of the data owned, the label of the data; and, the task information that each enterprise user hopes to complete based on federal learning, such as: face recognition, loan risk control, product recommendation, etc., extract data information (The amount of data owned by the enterprise, the data characteristics it owns, the label of the data) and the task information (face recognition, loan risk control, product recommendation, etc.) that each enterprise user wants to complete based on federal learning, as the current federal learning alliance Member information of each alliance member.
  • federal learning such as: face recognition, loan risk control, product recommendation, etc.
  • Step S400 Construct a federated learning model based on the member information, and manage the federated learning model.
  • the member information of each alliance member the data information and task information of each alliance member, construct a federated learning model suitable for the federated learning of each alliance member in the current federal learning alliance , And manage the constructed federated learning model after the construction of the federated learning model is completed.
  • the enterprise users want to be based on the federation Information on the tasks completed by learning-face recognition, loan risk control, product recommendation and other member information, as well as the expenses that each alliance member can bear-communication, time, computing resource expenses and hopes to obtain benefits based on current federal learning- -Model performance requirements, model performance improvements, etc., determine the cost and benefit distribution of alliance members (federated learning participants) in the current federated learning alliance (for example, according to the amount of data of each participant and the contribution to the federated learning model Determine the reward distribution, based on the regression model, neural network prediction model, neural network classification model, etc., determine the distribution strategy of the expenses and benefits of each alliance member), so as to construct a federated learning suitable for each alliance member in the current federated learning alliance
  • the federated learning model after completing the construction of the federated learning model
  • step S400 includes:
  • Step S401 Design the model structure of the federated learning model based on the extracted member information.
  • the federated learning method of the present application can also design the model structure of the federated learning model based on an existing deep learning model that has been proven in practice.
  • Step S402 After completing the model structure design of the federated learning model, initialize the federated learning model.
  • the model parameters of the current federated learning model that has been designed are initialized through random initialization to ensure that the currently constructed federated learning model can It is suitable for all members of the current federal learning alliance to conduct federal learning.
  • This application obtains the federated learning request input by the enterprise user from the input interface set by the federated learning method device when it is detected that the enterprise user inputs the federated learning request based on the above-mentioned federated learning method device; On the input interface of, after obtaining the federated learning request input by the enterprise user, from the obtained federated learning request, extract the requested items of the federated learning model that the enterprise user requests to participate in the federated learning request, and Further analyze whether the requested items can be executed by the constructed federated learning model; when the analysis determines that the federated learning model included in the federated learning request includes the requested items of the federated learning model requested by the enterprise user, the federated learning model can be constructed When executed, it detects the enterprise label information of the enterprise user who entered the federated learning request carried in the federated learning request, and from the preset enterprise information database according to the enterprise label information of the enterprise user carried in the detected federated learning request Among all the saved enterprise users, find and determine the target enterprise
  • the suitable enterprise is selected as the partner of the enterprise that submitted the federated learning request, so as to form a federated learning alliance.
  • the member information carried by the members of the federated learning alliance is constructed into a federated learning model suitable for the federated learning of the federated members. Therefore, companies that do not need to submit a federal learning request communicate with other companies one by one on the spot to persuade other companies to build a federal learning model as their partners, and save the manpower and material resources in the process of building a federal learning model for companies wishing to conduct federal learning. And time resources, reducing the cost of building a federated learning model.
  • managing the federated learning model includes:
  • Step S403 Manage the model training and model update of the constructed federated learning model.
  • the federated learning model is trained and modeled according to the management request of each federated member based on the federated learning method and equipment. Update management, or, based on a preset management strategy, model training and model update management of the currently constructed federated learning model.
  • step S403 includes:
  • step S4031 it is detected that the acquired federated learning request is a management request, and the federated learning model is subjected to model training and model update management according to the management request.
  • the federated learning request input by the enterprise user obtained from the input interface set by the federated learning method device is a request to manage the federated learning model serving the current enterprise user, update according to the input of the enterprise user
  • the requested content, model training and model update of the federated learning model serving current enterprise users is a request to manage the federated learning model serving the current enterprise user.
  • the federated learning service request entered in the input window of the set service request is specifically for the federated learning that has been constructed to serve the current enterprise user
  • the model is updated for the management request, it is based on the content of the management request input by the enterprise user.
  • the content of the management request input by the user is: model training for the current federated learning model, then according to the management request, manage federated learning Retrain the model, continue training, or manage the federated learning model to redesign and train the model, or the content of the management request entered by the user is: update the current federated learning alliance members (federated learning participants) , According to the current update request, from the preset enterprise information database, re-search and confirm the target enterprise as the new alliance member, that is, re-select the federal learning participant.
  • Step S4032 periodically perform model training and model update management on the federated learning model.
  • the preset model management strategy is a model management strategy that is set in advance based on the stability and other characteristics of the constructed federated learning model to perform periodic update processing on the current federated learning model.
  • the federated learning model is automatically trained and updated according to a predetermined time period (for example, one week), wherein the management of the federated learning model's model training includes at least: management Retraining and continuing training of the federated learning model, or managing the redesign and training of the federated learning model; the update processing of the federated learning model includes: updating the model parameters, that is, retraining the current federated learning model, for example, A neural network structure retrains the parameters of the neural network to obtain new model parameters of the current federated learning model; the update of the model structure means updating the model structure of the current federated learning model and retraining, for example, changing the structure of the neural network and re-training. Train the parameters of the neural network to obtain a new federated learning model.
  • the management of the federated learning model's model training includes at least: management Retraining and continuing training of the federated learning model, or managing the redesign and training of the federated learning model
  • the update processing of the federated learning model includes: updating
  • the federated learning model will be trained according to the management request of each federated member based on the federated learning method and equipment. And model update management, or, based on the preset model management strategy, the currently constructed federated learning model training and model update management.
  • the management is performed according to the input of the enterprise user
  • the requested content manages the model training and model update of the federated learning model serving current enterprise users; and, from the time the federated learning model is completed, the federated learning model is automatically trained and updated according to the predetermined time period.
  • the management of the model training of the federated learning model includes: managing the retraining and continuing training of the federated learning model, or managing the redesign and training of the federated learning model
  • the update processing of the federated learning model includes: updating of model parameters And model structure updates.
  • the model training and model of the federated learning model Perform management more, or automatically and periodically manage the model training and model update of the constructed federated learning model according to the pre-established model management strategy, thereby ensuring that the constructed federated learning model is in the process of serving enterprise users
  • the high efficiency saves the cost of updating the model independently by requesters participating in federated learning, and further improves the efficiency of creating federated learning models.
  • the federated learning method of this application further includes:
  • Step A serving the alliance members of the federated learning alliance according to the request items of the federated learning request.
  • the enterprise user wants to provide services to the current enterprise user or other alliance members of the federated learning alliance where the current enterprise user is located.
  • the federated learning service request entered in the input window of the set service request is specifically for the federated learning that has been constructed to serve the current enterprise user
  • the model is updated for the update request
  • the currently constructed federated learning model is updated; further, based on the current enterprise user’s input hope to perform federated learning training to complete the service request of loan risk control prediction operation, the current Enterprise users provide the prediction results obtained from the federated learning model training; further, when the enterprise users who wish to participate in the federated learning carry a local training model, based on the federated learning model parameter request input by the enterprise user, provide the current enterprise user
  • the model parameters and the federated learning code of the currently constructed federated learning model are available for current enterprise users to perform local machine learning based on their own data.
  • the analysis detects that the obtained enterprise user is based on the input window set on the PC terminal for the enterprise user to input the service request
  • the federated learning service request inputted by the enterprise user
  • the problem solved by federated learning cannot be solved by constructing a federated learning model, and/or the built federated learning model cannot meet the requirements such as the performance indicators of the learning model requested by enterprise users, use the current federated learning method on the device .
  • the set output interface (specifically, the feedback window for inputting service requests for enterprise users) outputs to the current enterprise users a prompt message that the "federated learning service request" is rejected.
  • This application provides services to the current enterprise user or other members of the federated learning alliance in which the enterprise user is located, based on the request items that the enterprise user enters in the federal learning request entered by the enterprise user obtained from the input interface, that is, to detect
  • the obtained federated learning service request input by the enterprise user is specifically an update request for updating the federated learning model that has been constructed to serve the current enterprise user
  • the federated learning method device can automatically find a federated learning partner for current enterprise users, design the model structure and initialize the federated learning model according to the request items contained in the service request, and then send the initial federated learning model to the service request.
  • Enterprise users, as well as the training and management of the completed federated learning model do not need to participate in federated learning for corporate users to independently carry out federated learning methods, training operations, management, and maintenance, which saves the manpower, material resources and resources of federated learning participants. Time cost improves the efficiency of federated learning methods.
  • an embodiment of the present application also proposes a federated learning system
  • the federated learning system includes:
  • the acquisition module is set to acquire the federated learning request received by the input interface
  • the alliance formation module is set to form a federal learning alliance according to the federal learning request
  • the detection module is configured to detect the member information of each alliance member in the federated learning alliance.
  • the model management module is configured to construct a federated learning model based on the member information and manage the federated learning model.
  • the federated learning system further includes:
  • the analysis module is configured to extract the requested items carried in the federated learning request and determine that the requested items can be executed.
  • the alliance building module includes:
  • the second detection unit is configured to detect the enterprise label information carried in the federal learning request after determining that the requested item can be executed;
  • the alliance formation subunit is set to match the target enterprise as the alliance member from the preset enterprise information database according to the enterprise label information to form a federal learning alliance.
  • the detection module includes:
  • the first detection unit is configured to detect all enterprise parameters stored in the preset enterprise information database
  • An indexing unit configured to index the enterprise label information of the target enterprise corresponding to each member of the alliance from all the enterprise parameters
  • the extraction unit is configured to extract the member information of each federated member required to form the federated learning alliance from the enterprise label information one by one.
  • model building module includes:
  • the design unit is set to design the model structure of the federated learning model based on the extracted member information
  • the initialization unit is set to initialize the federation learning model after completing the model structure design of the federation learning model.
  • model management module includes:
  • the management unit is configured to manage the model training and model update of the constructed federated learning model.
  • the management unit includes:
  • the first management subunit is set to detect that the acquired federated learning request is a management request, and perform model training and model update management on the federated learning model according to the management request;
  • the second management subunit is configured to periodically perform model training and model update management on the federated learning model according to a preset management strategy.
  • the federated learning system further includes:
  • the service module is configured to provide services to the alliance members of the federated learning alliance according to the request items of the federated learning request.
  • the service module includes:
  • the model distribution unit is configured to distribute the completed federated learning model to the alliance members of the federated learning alliance; or,
  • the parameter supply unit is configured to provide model training parameters to the alliance members for the alliance members to independently perform model training.
  • the embodiment of the present application also proposes a storage medium that is applied to a computer, that is, the storage medium is a computer-readable storage medium, and the computer-readable storage medium may be a non-volatile readable storage medium.
  • Computer readable instructions are stored on the medium, and when the computer readable instructions are executed by a processor, the steps of the federated learning method as described above are realized.
  • the method of the above embodiments can be implemented by means of software plus the necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is better. ⁇
  • the technical solution of this application essentially or the part that contributes to the existing technology can be embodied in the form of a software product, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, The optical disc) includes a number of instructions to enable a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the method described in each embodiment of the present application.

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Abstract

L'invention concerne un procédé d'apprentissage de fédération, un système, un dispositif terminal et un support d'informations, se rapportant au domaine technique de la technologie financière (Fintech). Le procédé d'apprentissage de fédération consiste : à obtenir une demande d'apprentissage fédérée reçue par une interface d'entrée (S100) ; à établir une alliance d'apprentissage fédérée selon la demande d'apprentissage fédérée (S200) ; à extraire des informations d'élément de chaque élément d'alliance dans l'alliance d'apprentissage fédérée (S300) ; à construire un modèle d'apprentissage fédéré sur la base des informations d'élément, et à gérer le modèle d'apprentissage fédéré (S400).
PCT/CN2019/116464 2019-07-17 2019-11-08 Procédé d'apprentissage de fédération, système, dispositif terminal, et support d'informations WO2021008017A1 (fr)

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WO2023040958A1 (fr) * 2021-09-18 2023-03-23 大唐移动通信设备有限公司 Procédé et appareil de traitement de groupe d'apprentissage fédéré ainsi qu'entité fonctionnelle
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