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Sociodynamics-inspired Adaptive Coalition and Client Selection in Federated Learning

Federated Learning (FL) enables privacy-preserving collaborative model training, yet its practical strength is often undermined by client data heterogeneity, which severely degrades model performance. This paper proposes that data heterogeneity across clients' distributions can be effectively addressed by adopting an approach inspired by opinion dynamics over temporal social networks. We introduce \shortname (Federated Coalition Variance Reduction with Boltzmann Exploration), a variance-reducing selection algorithm in which (1) clients dynamically organize into non-overlapping clusters based on asymptotic agreements, and (2) from each cluster, one client is selected to minimize the expected variance of its model update. Our experiments show that in heterogeneous scenarios our algorithm outperforms existing FL algorithms, yielding more accurate results and faster convergence, validating the efficacy of our approach.

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