Federated PAC-Bayesian Learning on Non-IID data
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 21 references that are in the map. First posted on arXiv 13 Sep 2023.
Builds on
11 of 21 references.
References · 11
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
- 2018Federated Learning with Non-IID Datacited by 1,108
- 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 387
- 2020MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysiscited by 206
- 2022Personalized Federated Learning via Variational Bayesian Inferencecited by 68
- 2022Towards Efficient Communications in Federated Learning: A Contemporary Surveycited by 24
- 2022Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learningcited by 14
- 2023More Communication Does Not Result in Smaller Generalization Error in Federated Learningcited by 10
- 2023Probably Approximately Correct Federated Learningcited by 4
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.