S-VOTE通过相似性投票选择客户端，降低通信开销与能耗。
S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning
- 基于客户端数据相似性动态投票选参与方，平衡资源使用。
- 通信成本降21%，收敛快4-6%，本地性能提升9-17%。
- 适合资源不均、数据异构的分布式学习场景。
去中心化联邦学习（DFL）可在无中心服务器情况下实现协作隐私训练，提升可扩展性与鲁棒性，但面临非独立同分布（non-IID）数据导致的模型性能下降、通信开销高和资源浪费等问题。本文提出S-VOTE机制，一种基于相似性的客户端投票选择方法，在非IID数据条件下优化资源利用并提升模型表现。该方法采用自适应本地训练策略，缓解参与度不均问题，使低利用率客户端可低成本贡献。在基准数据集上的大量实验表明，S-VOTE相比基线方法可降低最多21%的通信开销，加速4-6%的收敛速度，提升9-17%的本地性能，同时减少14-24%的能耗，有效应对异构环境中的挑战。
原文摘要 · Abstract (English)
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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