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This paper explores the challenges of implementing Federated Learning (FL) inpractical scenarios featuring isolated nodes with data heterogeneity, which canonly be connected to the server through wireless links in aninfrastructure-less environment. To overcome these challenges, we propose anovel mobilizing personalized FL approach, which aims to facilitate mobilityand resilience. Specifically, we develop a novel optimization algorithm calledRandom Walk Stochastic Alternating Direction Method of Multipliers (RWSADMM).RWSADMM capitalizes on the server's random movement toward clients andformulates local proximity among their adjacent clients based on hardinequality constraints rather than requiring consensus updates or introducingbias via regularization methods. To mitigate the computational burden on theclients, an efficient stochastic solver of the approximated optimizationproblem is designed in RWSADMM, which provably converges to the stationarypoint almost surely in expectation. Our theoretical and empirical resultsdemonstrate the provable fast convergence and substantial accuracy improvementsachieved by RWSADMM compared to baseline methods, along with its benefits ofreduced communication costs and enhanced scalability.

Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM