S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913673215213568 |
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| author | Sánchez, Pedro Miguel Sánchez Beltrán, Enrique Tomás Martínez Feng, Chao Bovet, Gérôme Pérez, Gregorio Martínez Celdrán, Alberto Huertas |
| author_facet | Sánchez, Pedro Miguel Sánchez Beltrán, Enrique Tomás Martínez Feng, Chao Bovet, Gérôme Pérez, Gregorio Martínez Celdrán, Alberto Huertas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19279 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning Sánchez, Pedro Miguel Sánchez Beltrán, Enrique Tomás Martínez Feng, Chao Bovet, Gérôme Pérez, Gregorio Martínez Celdrán, Alberto Huertas Machine Learning Distributed, Parallel, and Cluster Computing 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. |
| title | S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2501.19279 |