A variance-reduction-based client selection with coalition clustering yields modest accuracy gains over baselines in heterogeneous federated learning, but its convergence guarantee rests on an assumption that the policy is already gradient-aligned.
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Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions
A variance-reduction-based client selection with coalition clustering yields modest accuracy gains over baselines in heterogeneous federated learning, but its convergence guarantee rests on an assumption that the policy is already gradient-aligned.