  While deep neural networks (DNNs) based personalized federated learning (PFL)
is demanding for addressing data heterogeneity and shows promising performance,
existing methods for federated learning (FL) suffer from efficient systematic
uncertainty quantification. The Bayesian DNNs-based PFL is usually questioned
of either over-simplified model structures or high computational and memory
costs. In this paper, we introduce FedSI, a novel Bayesian DNNs-based
subnetwork inference PFL framework. FedSI is simple and scalable by leveraging
Bayesian methods to incorporate systematic uncertainties effectively. It
implements a client-specific subnetwork inference mechanism, selects network
parameters with large variance to be inferred through posterior distributions,
and fixes the rest as deterministic ones. FedSI achieves fast and scalable
inference while preserving the systematic uncertainties to the fullest extent.
Extensive experiments on three different benchmark datasets demonstrate that
FedSI outperforms existing Bayesian and non-Bayesian FL baselines in
heterogeneous FL scenarios.
