Bayesian personalized federated learning with shared and personalized uncertainty representations
Bayesian personalized federated learning (BPFL) addresses challenges in existing
personalized FL (PFL). BPFL aims to quantify the uncertainty and heterogeneity within and
across clients towards uncertainty representations by addressing the statistical
heterogeneity of client data. In PFL, some recent preliminary work proposes to decompose
hidden neural representations into shared and local components and demonstrates
interesting results. However, most of them do not address client uncertainty and …
personalized FL (PFL). BPFL aims to quantify the uncertainty and heterogeneity within and
across clients towards uncertainty representations by addressing the statistical
heterogeneity of client data. In PFL, some recent preliminary work proposes to decompose
hidden neural representations into shared and local components and demonstrates
interesting results. However, most of them do not address client uncertainty and …
[CITATION][C] Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations. arXiv 2023
H Chen, H Liu, L Cao, T Zhang - arXiv preprint arXiv:2309.15499
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