FedOBP: Federated Optimal Brain Personalization through Cloud-Edge Element-wise Decoupling

arXiv:2604.16574v1 Announce Type: cross Abstract: 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 personal