Federal learning-based data processing method and apparatus, and computer device

Abstract

The invention relates to a federal learning-based data processing method and device, computer equipment, a storage medium and a computer program product. The method can be applied to various scenes such as a cloud technology, artificial intelligence, smart traffic, auxiliary driving and the like, for example, the method is applied to parameter servers in a federated learning architecture, and comprises the following steps: receiving gradient data obtained by participants in a corresponding participant cluster by utilizing local data training; summarizing the gradient data of the participant cluster to obtain gradient summarized data; obtaining a topological structure of a parameter server; exchanging gradient summary data with an adjacent parameter server based on the parameter server topological structure; and updating the parameters of the joint model according to the gradient summarized data of all the parameter servers obtained by exchange. According to the method, the system stability is improved, and the topological structure of the parameter servers is constructed based on the response time between the parameter servers and is not fixed, so that a basis is provided for improving the data exchange efficiency between the parameter servers.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular, to a data processing method, apparatus, computer equipment, storage medium and computer program product based on federated learning. Background technique [0002] Federated Learning (Federated Learning) is an emerging artificial intelligence basic technology, which is a distributed machine learning training method based on privacy protection. On the end, it can train a high-quality prediction model while protecting the privacy of client data. [0003] A federated learning architecture such as figure 1 As shown, it is a single-parameter server, multi-participant federated learning structure. As the name suggests, the parameter server has only a single node, which exchanges model parameters with multiple participants respectively. The specific process is as figure 1 shown. Single-parameter server and multi-participant structure, the parameter server...

AI Technical Summary

Problems solved by technology

Single parameter server, multi-participant structure, the parameter server can easily become the performance bottleneck in the entire training process, and because the parameter server is a single point, the robustness of the entire training process will be low, if the parameter server fails or Network problems, then the entire federated learning training process will have problems

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