AI RESEARCH
FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs
arXiv CS.LG
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ArXi:2605.21264v1 Announce Type: new Federated Learning (FL) has emerged as a promising paradigm for privacy-preserving distributed learning. However, existing FL methods face a fundamental challenge. Traditional averaging-based approaches suffer from parameter divergence under non-IID conditions, while personalized FL methods overfit to local data and fail to generalize to new clients (cold-start problem). Mixture-of-Experts naturally addresses this by routing heterogeneous data to specialized experts rather than forcing uniform aggregation.