Privacy-preserving asynchronous federated learning under non-IID settings

Publication Type

Journal Article

Publication Date

1-2024

Abstract

To address the challenges posed by data silos and heterogeneity in distributed machine learning, privacy-preserving asynchronous Federated Learning (FL) has been extensively explored in academic and industrial fields. However, existing privacy-preserving asynchronous FL schemes still suffer from the problem of low model accuracy caused by inconsistency between delayed model updates and current model updates, and even cannot adapt well to Non-Independent and Identically Distributed (Non-IID) settings. To address these issues, we propose a Privacy-preserving Asynchronous Federated Learning based on the alternating direction multiplier method (PAFed), which is able to achieve high-accuracy models in Non-IID settings. Specifically, we utilize vector projection techniques to correct the inconsistency between delayed model updates and current model updates, thereby reducing the impact of delayed model updates on the aggregation of current model updates. Additionally, we employ an optimization method based on alternating direction multipliers to adapt the Non-IID settings to further enhance the global model accuracy. Finally, through extensive experiments, we demonstrate that our scheme improves the model accuracy by up to 12.53% when compared with current state-of-the-art solution FedADMM.

Keywords

Adaptation models, asynchronous, Computational modeling, Data models, Federated learning, Federated learning, Non-IID, Optimization, Privacy, Privacy-preserving, Vectors

Discipline

Information Security

Research Areas

Cybersecurity

Publication

IEEE Transactions on Information Forensics and Security

ISSN

1556-6013

Identifier

10.1109/TIFS.2024.3402149

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TIFS.2024.3402149

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