Joint Optimization for Federated Learning and Transmission over Unreliable Wireless Networks with Heterogeneous Data
In wireless federated learning (FL), data heterogeneity and multiple local updates induce client drift, degrading model convergence. It is further affected by unreliable wireless links, as transmission errors may invalidate model updates. To address these challenges, we propose a federated random walk averaging (FedRW)...
Chang-Heng Wang, Xian-Chao Zhang, Zhi-Qing Wei et al.
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