AI RESEARCH
Optimizing Stochastic Gradient Push under Broadcast Communications
arXiv CS.LG
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ArXi:2604.15549v1 Announce Type: new We consider the problem of minimizing the convergence time for decentralized federated learning (DFL) in wireless networks under broadcast communications, with focus on mixing matrix design. The mixing matrix is a critical hyperparameter for DFL that simultaneously controls the convergence rate across iterations and the communication demand per iteration, both strongly influencing the convergence time.