This paper explores the challenges of implementing Federated Learning (FL) in practical scenarios featuring isolated nodes with data heterogeneity, which can only be connected to the server through wireless links in an infrastructure-less environment. To overcome these challenges, we propose a novel mobilizing personalized FL approach, which aims to facilitate mobility and resilience. Specifically, we develop a novel optimization algorithm called Random Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM). RWSADMM capitalizes on the server's random movement toward clients and formulates local proximity among their adjacent clients based on hard inequality constraints rather than requiring consensus updates or introducing bias via regularization methods. To mitigate the computational burden on the clients, an efficient stochastic solver of the approximated optimization problem is designed in RWSADMM, which provably converges to the stationary point almost surely in expectation. Our theoretical and empirical results demonstrate the provable fast convergence and substantial accuracy improvements achieved by RWSADMM compared to baseline methods, along with its benefits of reduced communication costs and enhanced scalability.

翻译：本文探讨了在实际场景中实施联邦学习（FL）所面临的挑战，这些场景存在孤立节点与数据异构性，且节点仅能通过无基础设施环境中的无线链路与服务器连接。为解决这些挑战，我们提出了一种新颖的移动化个性化联邦学习方法，旨在提升移动性与鲁棒性。具体而言，我们开发了一种名为随机游走交替方向乘子法（RWSADMM）的新型优化算法。RWSADMM利用服务器向客户端的随机移动，并基于硬不等式约束而非要求一致更新或通过正则化方法引入偏差，来建立相邻客户端之间的局部邻近性。为减轻客户端的计算负担，RWSADMM设计了近似优化问题的高效随机求解器，该求解器在期望意义上几乎必然收敛至驻点。我们的理论与实证结果表明，与基线方法相比，RWSADMM具有可证明的快速收敛性及显著的精度提升，同时能降低通信成本并增强可扩展性。