S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

• URL: http://arxiv.org/abs/2501.19279v1
• Date: Fri, 31 Jan 2025 16:43:02 GMT
• Title: S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
• Authors: Pedro Miguel Sánchez Sánchez, Enrique Tomás Martínez Beltrán, Chao Feng, Gérôme Bovet, Gregorio Martínez Pérez, Alberto Huertas Celdrán,
• Abstract summary: Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server.<n>This work proposes S-VOTE to address DFL challenges in heterogeneous environments.
• Score: 10.326889154205157
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server. This decentralized approach reduces bottlenecks and eliminates single points of failure, enhancing scalability and resilience. However, DFL also introduces challenges such as suboptimal models with non-IID data distributions, increased communication overhead, and resource usage. Thus, this work proposes S-VOTE, a voting-based client selection mechanism that optimizes resource usage and enhances model performance in federations with non-IID data conditions. S-VOTE considers an adaptive strategy for spontaneous local training that addresses participation imbalance, allowing underutilized clients to contribute without significantly increasing resource costs. Extensive experiments on benchmark datasets demonstrate the S-VOTE effectiveness. More in detail, it achieves lower communication costs by up to 21%, 4-6% faster convergence, and improves local performance by 9-17% compared to baseline methods in some configurations, all while achieving a 14-24% energy consumption reduction. These results highlight the potential of S-VOTE to address DFL challenges in heterogeneous environments.

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