AI RESEARCH
FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning
arXiv CS.LG
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ArXi:2603.28006v1 Announce Type: new Statistical heterogeneity in Federated Learning (FL) often leads to negative transfer, where a single global model fails to serve diverse client distributions. Personalized federated learning (pFL) aims to address this by tailoring models to individual clients. However, under most existing pFL approaches, clients integrate peer client contributions uniformly, which ignores the reality that not all peers are likely to be equally beneficial.