让少量移动用户帮忙传数据，能显著提升去中心化联邦学习效果
Distribution-Aware Mobility-Assisted Decentralized Federated Learning
- 设计基于数据分布的智能移动策略，引导移动客户端高效传播模型信息
- 实验表明，仅少量移动客户端即可使准确率显著提升，优于随机移动
- 适合研究移动边缘计算、物联网场景下的分布式学习系统
去中心化联邦学习（DFL）因其可扩展性和无需中心服务器而备受关注。实际中部分参与客户端具有移动性，但用户移动对DFL性能的影响尚未充分探索，尽管其可能促进通信与模型收敛。本文证明，引入少量移动客户端，即使采用随机移动，也能显著提升DFL准确率，促进信息流动。为进一步优化，提出新型分布感知移动模式，移动客户端根据数据分布和静态客户端位置进行策略性移动，有效缓解数据异构性，加速学习收敛。大量实验验证了引入移动性的有效性，并证明所提移动策略优于随机移动。
原文摘要 · Abstract (English)
Decentralized federated learning (DFL) has attracted significant attention due to its scalability and independence from a central server. In practice, some participating clients can be mobile, yet the impact of user mobility on DFL performance remains largely unexplored, despite its potential to facilitate communication and model convergence. In this work, we demonstrate that introducing a small fraction of mobile clients, even with random movement, can significantly improve the accuracy of DFL by facilitating information flow. To further enhance performance, we propose novel distribution-aware mobility patterns, where mobile clients strategically navigate the network, leveraging knowledge of data distributions and static client locations. The proposed moving strategies mitigate the impact of data heterogeneity and boost learning convergence. Extensive experiments validate the effectiveness of induced mobility in DFL and demonstrate the superiority of our proposed mobility patterns over random movement.
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