- Title
- Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
- Creator
- Huang, Victoria; Sohail, Shaleeza; Mayo, Michael; Botran, Tania Lorido; Rodrigues, Mark; Anderson, Chris; Ooi, Melanie
- Relation
- 2023 IEEE 16th International Conference on Cloud Computing (CLOUD). Proceedings of the IEEE 16th International Conference on Cloud Computing (IEEE CLOUD) (IL, Chicago 02-08 July, 2023) p. 141-143
- Publisher Link
- http://dx.doi.org/10.1109/CLOUD60044.2023.00024
- Publisher
- IEEE COMPUTER SOC
- Resource Type
- conference paper
- Date
- 2023
- Description
- 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.
- Subject
- federated learning; fault tolerance; unreliable clients; robustness; rural environment
- Identifier
- http://hdl.handle.net/1959.13/1495752
- Identifier
- uon:54065
- Identifier
- ISBN:9798350304824
- Identifier
- ISSN:2159-6182
- Language
- eng
- Reviewed
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