Federated learning training method and device for high-delay network environment robustness

Abstract

The invention discloses a federated learning training method and device for high-delay network environment robustness, computer equipment and a storage medium, and relates to the artificial intelligence technology. The method comprises: obtaining the current system time, and obtaining a corresponding target data uploading terminal if the encrypted data uploaded by a plurality of data uploading terminals is not received; obtaining a current network time delay value of each target data uploading terminal to obtain a maximum network time delay value; calculating to obtain a delay step length according to the maximum network delay value and the unit time sequence interval step length; summing the current system time and the delay step length to obtain target system time; and if the current time is the target system time and the target encrypted data uploaded by the target data uploading terminal is not received, stopping the local federation learning training until the target encrypted data uploaded by all the target data uploading terminals is received, and recovering the local federation learning training. Under the condition of network delay, the training efficiency of federated learning is kept in a time delay sparse updating mode.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence model hosting, and in particular to a federated learning training method, device, computer equipment and storage medium robust to high-delay network environments. Background technique [0002] Federated machine learning is a machine learning framework based on distributed parameter aggregation technology. It focuses on distributed multi-users and the corresponding federated parameter aggregation mechanism. It can effectively help multiple organizations to 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. [0003] The current mainstream f...

AI Technical Summary

Problems solved by technology

[0005] The embodiment of the present invention provides a federated learning training method, device, computer equipment and storage medium robust in a high-delay network environment, aiming to solve the problem of the synchronous stochastic gradient descent method of federated learning in the prior art when the network delay is relatively serious , the problem that the training efficiency is greatly reduced

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