Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients

• URL: http://arxiv.org/abs/2305.09856v1
• Date: Tue, 16 May 2023 23:55:47 GMT
• Title: Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
• Authors: Victoria Huang, Shaleeza Sohail, Michael Mayo, Tania Lorido Botran, Mark Rodrigues, Chris Anderson, Melanie Ooi
• Abstract summary: Federated learning (FL) enables decentralized model training across multiple devices without exposing their local training data. We show that simple FL algorithms can perform surprisingly well in the presence of unreliable clients.
• Score: 0.28939699256527274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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