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 heterogeneity in FL systems,
while appropriately decoupling neural representations is challenging and often
ad hoc. In this paper, we make the first attempt to introduce a general BPFL
framework to decompose and jointly learn shared and personalized uncertainty
representations on statistically heterogeneous client data over time. A
Bayesian federated neural network BPFed instantiates BPFL by jointly learning
cross-client shared uncertainty and client-specific personalized uncertainty
over statistically heterogeneous and randomly participating clients. We further
involve continual updating of prior distribution in BPFed to speed up the
convergence and avoid catastrophic forgetting. Theoretical analysis and
guarantees are provided in addition to the experimental evaluation of BPFed
against the diversified baselines.

Bayesian 个性化联邦学习（BPFL）通过统计上异质的客户数据来量化客户内部及客户间的不确定性和异质性，并将隐藏的神经表示分解成共享和本地组件，并联合学习共享和个性化的不确定性表示。