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Federal learning scheduling method and device and system

A scheduling method and federated technology, applied in the information field, can solve the problems of slow model update speed and low flexibility of server scheduling and client

Pending Publication Date: 2021-03-16
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The purpose of the embodiment of this application is to propose a federated learning scheduling method, device and system to solve the problems of slow model update speed and low flexibility of server scheduling clients in traditional federated learning methods

Method used

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  • Federal learning scheduling method and device and system
  • Federal learning scheduling method and device and system
  • Federal learning scheduling method and device and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0081] refer to figure 1 , shows a flow chart of an embodiment of the federated learning scheduling method according to the present application, and for the convenience of description, only the parts related to the present application are shown.

[0082] In step S1, the server receives the federated learning training request sent by the client.

[0083] In this embodiment, the federated learning training request is a request sent by the client to the server whether to participate in federated learning training, so that the server can periodically schedule the client to participate in model training.

[0084] In step S2, the server responds to the federated learning training request and sends the model data of the current global model to the client for training.

[0085] In this embodiment, the model data of the current global model includes the global model of the server such as W t and a version number such as t.

[0086] In this embodiment, sending the model data of the c...

Embodiment 2

[0122] further reference Figure 5 , shows a flow chart of another embodiment of the federated learning scheduling method according to the present application, which is applied to the server and at least one client connected to the server. For the convenience of description, only the parts related to the present application are shown.

[0123] In step S501, the client sends a federated learning training request to the server.

[0124] In step S502, when the server receives the federated learning training request sent by the client, the server sends the model data of the current global model to the client for training.

[0125]In step S503, when the client receives the model data sent by the server, the client performs training based on the model data to obtain the trained client model and client version data corresponding to the client model.

[0126] In step S504, when the training is completed, the client records the time when the training is completed to obtain the model t...

Embodiment 3

[0142] further reference Figure 7 , as for the above figure 1 To realize the method shown, this application provides an embodiment of a federated learning scheduling device, which is the same as figure 1 Corresponding to the illustrated method embodiments, the apparatus can be specifically applied to various electronic devices.

[0143] Such as Figure 7 As shown, the federated learning scheduling device 100 of this embodiment includes: a request receiving module 101 , a data sending module 102 , a training time acquiring module 103 , a preparation value calculating module 104 , a time window setting module 105 and a global model updating module 106 . in:

[0144] The request receiving module 101 is configured to receive the federated learning training request sent by the client;

[0145] The data sending module 102 is used to respond to the federated learning training request and send the model data of the current global model to the client for training;

[0146] The tr...

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Abstract

The embodiment of the invention belongs to the technical field of information, and relates to a federated learning scheduling method, which comprises the steps that a server receives a federated learning training request sent by a client; the server responds to the federated learning training request and sends the model data of the current global model to the client for training; the server schedules the historical training record of the client to obtain the historical training time of the client; the server calculates a preparation value of the time window based on the historical training time; when the server receives a training report sent by the client, setting a training time window based on the preparation value; and the server performs model aggregation operation based on the data in the training reports sent by all the clients and received in the training time window to obtain a new global model. The invention further provides a federated learning scheduling device and system.According to the method, the server can flexibly schedule the client, the accuracy of the training model can be improved, and the training time can be saved.

Description

technical field [0001] The present application relates to the field of information technology, and in particular to a federated learning scheduling method, device and system. Background technique [0002] Federated Learning Federated Machine Learning is a machine learning framework that can effectively help multiple organizations conduct data usage and machine learning modeling while meeting the requirements of user privacy protection, data security, and government regulations. As a distributed machine learning paradigm, federated learning can effectively solve the problem of data islands, allowing participants to jointly model without sharing data, technically breaking data islands, and realizing AI collaboration. The general process is that the user performs training on the local client, and then sends the trained model to the server. The server performs certain processing on the model, and then distributes it to the user's client, and the client performs training based on...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N20/20G06K9/62
CPCG06N20/20G06F18/23
Inventor 史国梅栗力陈文艳须成忠
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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