Federated learning meets Bayesian neural network: Robust and uncertainty-aware distributed variational inference.

Area of Science:

• Machine Learning
• Artificial Intelligence
• Data Privacy

Background:

• Federated Learning (FL) is widely used for data privacy but struggles with limited client data and heterogeneity, leading to suboptimal aggregated models.
• Client-side model overfitting and drift are significant challenges in traditional FL frameworks.
• Existing FL methods often fail to adequately address the complexities of distributed, heterogeneous datasets.

Purpose of the Study:

• To introduce a novel Federated Learning approach integrating Bayesian Neural Networks (BNNs) to enhance model robustness and data privacy.
• To address key challenges in fusing BNNs with FL, including prior selection, weight aggregation, and variance management.
• To improve performance of both global and personalized models in a simulated FL environment.

Main Methods:

• Developed FedUAB (FL with uncertainty-aware BNNs), where clients train BNNs using the Bayes by backprop algorithm.
• Modeled BNN weights as Gaussian distributions to mitigate overfitting and enhance data privacy.
• Implemented novel methods for prior distribution selection, Gaussian weight aggregation, and variance management.

Main Results:

• FedUAB demonstrated superior performance over traditional FL and other Bayesian FL methods in simulations.
• The approach enhanced robustness and mitigated overfitting issues inherent in FL.
• FedUAB models effectively quantified and decomposed uncertainties, providing valuable insights.

Conclusions:

• The FedUAB approach offers a robust solution for privacy-preserving distributed machine learning by integrating uncertainty-aware BNNs.
• This method effectively tackles data limitations and heterogeneity challenges in FL.
• FedUAB provides enhanced performance and uncertainty quantification capabilities for FL systems.