Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 44 references that are in the map. First posted on arXiv 16 May 2023.
Builds on
21 of 44 references.
References · 21
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9 more
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.