Trustworthy Personalized Bayesian Federated Learning via Posterior
  Fine-Tune
Mengen Luo Chi Xu Ercan Engin Kuruoglu
Performance degradation owing to data heterogeneity and low output
interpretability are the most significant challenges faced by federated
learning in practical applications. Personalized federated learning diverges
from traditional approaches, as it no longer seeks to train a single model, but
instead tailors a unique personalized model for each client. However, previous
work focused only on personalization from the perspective of neural network
parameters and lack of robustness and interpretability. In this work, we
establish a novel framework for personalized federated learning, incorporating
Bayesian methodology which enhances the algorithm's ability to quantify
uncertainty. Furthermore, we introduce normalizing flow to achieve
personalization from the parameter posterior perspective and theoretically
analyze the impact of normalizing flow on out-of-distribution (OOD) detection
for Bayesian neural networks. Finally, we evaluated our approach on
heterogeneous datasets, and the experimental results indicate that the new
algorithm not only improves accuracy but also outperforms the baseline
significantly in OOD detection due to the reliable output of the Bayesian
approach.