  Federated learning's poor performance in the presence of heterogeneous data
remains one of the most pressing issues in the field. Personalized federated
learning departs from the conventional paradigm in which all clients employ the
same model, instead striving to discover an individualized model for each
client to address the heterogeneity in the data. One of such approach involves
personalizing specific layers of neural networks. However, prior endeavors have
not provided a dependable rationale, and some have selected personalized layers
that are entirely distinct and conflicting. In this work, we take a step
further by proposing personalization at the elemental level, rather than the
traditional layer-level personalization. To select personalized parameters, we
introduce Bayesian neural networks and rely on the uncertainty they offer to
guide our selection of personalized parameters. Finally, we validate our
algorithm's efficacy on several real-world datasets, demonstrating that our
proposed approach outperforms existing baselines.
