AI RESEARCH
FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling
arXiv CS.LG
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ArXi:2604.16574v1 Announce Type: new Federated Learning (FL) faces challenges from client data heterogeneity and resource-constrained mobile devices, which can degrade model accuracy. Personalized Federated Learning (PFL) addresses this issue by adapting shared global knowledge to local data distributions. A promising approach in PFL is model decoupling, which separates the model into global and personalized parameters, raising the key question of which parameters should be personalized to balance global knowledge sharing and local adaptation.