AI RESEARCH
Fed-BAC: Federated Bandit-Guided Additive Clustering in Hierarchical Federated Learning
arXiv CS.LG
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ArXi:2605.11815v1 Announce Type: new Hierarchical federated learning (HFL) leverages edge servers for partial aggregation in edge computing. Yet existing FL methods lack mechanisms for jointly optimizing cluster assignment and client selection under data heterogeneity. This paper proposes Fed-BAC, which integrates additive cluster personalization with a two-level bandit framework: contextual bandits at the cloud learn server-to-cluster assignments, while Thompson Sampling at each edge server identifies high-contributing clients.