Robustness federated learning algorithm based on partial parameter aggregation

Abstract

The invention belongs to the technical field of federated learning robustness, and particularly relates to a robustness federated learning algorithm based on partial parameter aggregation, which comprises a basic partial aggregation protocol and a security partial aggregation algorithm based on homomorphic encryption. Aiming at the problem that a server or a third-party mechanism is difficult to detect malicious users and is difficult to resist backdoor attacks from a client in a federated learning training scene, a partial aggregation protocol is designed, the capability of attacking the users by malicious backdoors is limited while stable convergence of a model is ensured, the robustness of a federated learning system is remarkably enhanced, and the invention is especially suitable for large-scale user joint training scenes. Meanwhile, in order to ensure privacy of data and models participating in training of clients, a security aggregation algorithm based on homomorphic encryption is designed for the aggregation algorithm of the part, and it is ensured that data uploaded by a user is invisible to a server. Therefore, the security of federal learning on the client side and the server side is ensured.

Description

technical field [0001] The present invention belongs to the research in the field of federated learning robustness, and specifically relates to a robust federated learning algorithm based on partial parameter aggregation, aiming at federated learning including a partial federated learning aggregation algorithm (PartialFedAvgalgorithm) and a secure aggregation encryption protocol based on partial aggregation (Partial Secure Aggregation Protocol). Background technique [0002] Federated Learning technology provides a security solution for massive end-user cooperative training models. Federated learning technology allows users to upload model parameters instead of directly uploading private data. At the same time, it is guaranteed that any uploaded data of the user is under the encryption protection of the security aggregation algorithm, which further protects the privacy of the user's data. In the federated learning process, the server first initializes a global model and di...

AI Technical Summary

Problems solved by technology

However, due to the application of secure aggregation algorithms in federated learning, it is difficult for the server to verify the correctness of the results uploaded by users

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