Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2024

Abstract

Federated Learning (FL) suffers from low convergence and significant accuracy loss due to local biases caused by non-Independent and Identically Distributed (non-IID) data. To enhance the non-IID FL performance, a straightforward idea is to leverage the Generative Adversarial Network (GAN) to mitigate local biases using synthesized samples. Unfortunately, existing GAN-based solutions have inherent limitations, which do not support non-IID data and even compromise user privacy. To tackle the above issues, we propose a GAN-based unbiased FL scheme, called FlGan, to mitigate local biases using synthesized samples generated by GAN while preserving user-level privacy in the FL setting. Specifically, FlGan first presents a federated GAN algorithm using the divide-and-conquer strategy that eliminates the problem of model collapse in non-IID settings. To guarantee user-level privacy, FlGan then exploits Fully Homomorphic Encryption (FHE) to design the privacy-preserving GAN augmentation method for the unbiased FL. Extensive experiments show that FlGan achieves unbiased FL with 10%−60%10%−60% accuracy improvement compared with two state-of-the-art FL baselines (i.e., FedAvg and FedSGD) trained under different non-IID settings. The FHE-based privacy guarantees only cost about 0.53% of the total overhead in FlGan.

Keywords

Federated learning, fully homomorphic encryption, GAN, non-IID, user-level privacy

Discipline

Databases and Information Systems | Information Security

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Knowledge and Data Engineering

Volume

36

Issue

4

First Page

1566

Last Page

1581

ISSN

1041-4347

Identifier

10.1109/TKDE.2023.3309858

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2023.3309858

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