Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
Machine Learning
2023-10-23 v1 Artificial Intelligence
Distributed, Parallel, and Cluster Computing
Abstract
Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unreliable devices (e.g., dropping out, misconfiguration, poor data quality) in real-world applications is not fully investigated. We carefully chose two representative, real-world classification problems with a limited numbers of clients to better analyze FL fault tolerance. Contrary to the intuition, simple FL algorithms can perform surprisingly well in the presence of unreliable clients.
Keywords
Cite
@article{arxiv.2305.09856,
title = {Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients},
author = {Victoria Huang and Shaleeza Sohail and Michael Mayo and Tania Lorido Botran and Mark Rodrigues and Chris Anderson and Melanie Ooi},
journal= {arXiv preprint arXiv:2305.09856},
year = {2023}
}