Robust federated learning training method and device in high-latency network environment

Abstract

The invention discloses a robust federated learning training method, device, computer equipment and storage medium in a high-delay network environment, and relates to artificial intelligence technology, including obtaining the current system time, and obtaining the corresponding The target data upload terminal; obtain the current network delay value of each target data upload terminal to obtain the maximum network delay value; calculate the delay step according to the maximum network delay value and the unit timing interval step; the current system time The target system time is obtained by summing the delay step length. If the current time is the target system time and the target encrypted data uploaded by the target data upload terminal has not been received, the local federated learning training will be stopped until all target data upload terminals are received. After the target encrypts the data, resume the local federated learning training. In the case of network delay, the training efficiency of federated learning is maintained through delay sparse update.

Description

technical field [0001] The invention relates to the technical field of model hosting of artificial intelligence, in particular to a robust federated learning training method, device, computer equipment and storage medium in a high-latency network environment. Background technique [0002] Federated machine learning is a machine learning framework based on distributed parameter aggregation technology. Its focus is on distributed multi-users and the corresponding federated parameter aggregation mechanism. It can effectively help multiple organizations conduct data usage and machine learning modeling while meeting user privacy protection, data security, and government regulations. As a distributed machine learning paradigm, federated learning can effectively solve the problem of data silos, allowing participants to jointly model without sharing data, which can technically break the data silos and realize AI collaboration. [0003] The current mainstream federated learning tech...

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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