Federated PAC-Bayesian Learning on Non-IID data

Zihao Zhao; Yang Liu; Wenbo Ding; Xiao-Ping Zhang

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arXiv · 2309.06683

Federated PAC-Bayesian Learning on Non-IID data

Zihao Zhao·Yang Liu·Wenbo Ding·Xiao-Ping Zhang

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Abstract

Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local data. This bound assumes unique prior knowledge for each client and variable aggregation weights. We also introduce an objective function and an innovative Gibbs-based algorithm for the optimization of the derived bound. The results are validated on real-world datasets.

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BibTeX RIS

Zihao Zhao, Yang Liu, Wenbo Ding, Xiao-Ping Zhang. 2023-09-13. Federated PAC-Bayesian Learning on Non-IID data. https://arxiv.org/abs/2309.06683

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