PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning
Published in NeurIPS 2023, 2023
Personalized FL (PFL), synthesizing personalized models from a global model via training on local data, may overlook the specific information that the clients have been sampled. In this paper, we propose a novel scheme to inject personalized prior knowledge into the global model in each client, which attempts to mitigate the introduced incomplete information problem in PFL.
Recommended citation: PRIOR: Personalized Prior for Reactivating the Information Overlooked in Federated Learning. NeurIPS 2023. M. Shi, Y. Zhou, K. Wang, H. Zhang, S. Huang, Q. Ye, J. Lv
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