A Huber Loss Minimization Approach to Byzantine Robust Federated Learning
Abstract
Federated learning systems are susceptible to adversarial attacks. To combat this, we introduce a novel aggregator based on Huber loss minimization, and provide a comprehensive theoretical analysis. Under independent and identically distributed (i.i.d) assumption, our approach has several advantages compared to existing methods. Firstly, it has optimal dependence on epsilon, which stands for the ratio of attacked clients. Secondly, our approach does not need precise knowledge of epsilon. Thirdly, it allows different clients to have unequal data sizes. We then broaden our analysis to include non-i.i.d data, such that clients have slightly different distributions.
Cite
Text
Zhao et al. "A Huber Loss Minimization Approach to Byzantine Robust Federated Learning." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I19.30181Markdown
[Zhao et al. "A Huber Loss Minimization Approach to Byzantine Robust Federated Learning." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/zhao2024aaai-huber/) doi:10.1609/AAAI.V38I19.30181BibTeX
@inproceedings{zhao2024aaai-huber,
title = {{A Huber Loss Minimization Approach to Byzantine Robust Federated Learning}},
author = {Zhao, Puning and Yu, Fei and Wan, Zhiguo},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {21806-21814},
doi = {10.1609/AAAI.V38I19.30181},
url = {https://mlanthology.org/aaai/2024/zhao2024aaai-huber/}
}