Federated Learning with Energy-Based Structured Probabilistic Inference

arXiv:2606.30161v1 Announce Type: new Abstract: Federated learning typically aggregates client updates using fixed or heuristic weighting rules, which can be suboptimal when clients have heterogeneous data and varying contributions to the global model. We propose a framework that refines client aggregation weights using Conditional Random Fields (CRFs). Our method defines unary potentials for individual clients and pairwise potentials for all client pairs, allowing the server to model both client-specific reliability and interactions between clients. The resulting CRF inference produces aggreg

Source: arXiv cs.LG — read the full report at the original publisher.

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