Federated learning method and apparatus, electronic device, and storage medium
AssigneeJingdong Technology Holding Co., Ltd.
InventorsWenxi ZHANG, Peiqi WANG, Songxiang GU
Life of this patent
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Abstract
A federated learning training method is performed by a server. The method comprises: receiving gradient information of a labeled sample of each client sent by each client; according to the gradient information sent by each client, obtaining target gradient information belonging to a same labeled sample; determining a client to which each labeled sample belongs; and sending, to a client to which the same labeled sample belongs, the target gradient information corresponding to the same labeled sample.
Claims27
A federated learning-based training method, performed by a server, the method comprising:
- receiving gradient information of a labeled sample sent by each client;
- obtaining, based on the gradient information sent by each client, target gradient information belonging to a same labeled sample;
- determining a client to which each labeled sample belongs;
- and
- sending the target gradient information corresponding to the same labeled sample to a client to which the same labeled sample belongs.
A federated learning-based training method, performed by a client, the method comprising:
- sending gradient information of a labeled sample of the client to a server after each training;
- receiving target gradient information of each labeled sample belonging to the client sent by the server;
- and
- obtaining a target federated learning model by updating, based on the target gradient information, a model parameter of a local learning model and performing a next training.
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An electronic device, comprising:
- at least one processor;
- and
- a memory communicatively connected to the at least one processor and stored with instructions executable by the at least one processor,
- wherein the at least one processor is caused to:
- send gradient information of a labeled sample of the client to a server after each training;
- receive target gradient information of each labeled sample belonging to the client sent by the server;
- and
- obtain a target federated learning model by updating, based on the target gradient information, a model parameter of a local learning model and performing a next training.
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Dependencies are inferred from the claim text.