A Secure and Private Distributed Bayesian Federated Learning Design

2026-02-23

AI Summary

This work addresses three critical challenges in decentralized federated learning: privacy leakage, slow convergence, and vulnerability to Byzantine attacks. The authors propose a novel decentralized framework that, for the first time, jointly models the interplay among dynamic network topology, privacy constraints, Byzantine robustness, and convergence rate. Central to this approach is a fully distributed, self-governed neighbor selection mechanism. By integrating Bayesian inference, graph neural networks, and reinforcement learning, the method simultaneously ensures strong privacy guarantees and Byzantine resilience while significantly accelerating convergence and reducing both communication and computational overhead. Experimental results demonstrate that the proposed framework outperforms existing state-of-the-art approaches in overall performance.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Search and Optimization: Distributed Search
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Decentralized Web and Fediverse systems
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Distributed Federated Learning (DFL) enables decentralized model training across large-scale systems without a central parameter server. However, DFL faces three critical challenges: privacy leakage from honest-but-curious neighbors, slow convergence due to the lack of central coordination, and vulnerability to Byzantine adversaries aiming to degrade model accuracy. To address these issues, we propose a novel DFL framework that integrates Byzantine robustness, privacy preservation, and convergence acceleration. Within this framework, each device trains a local model using a Bayesian approach and independently selects an optimal subset of neighbors for posterior exchange. We formulate this neighbor selection as an optimization problem to minimize the global loss function under security and privacy constraints. Solving this problem is challenging because devices only possess partial network information, and the complex coupling between topology, security, and convergence remains unclear. To bridge this gap, we first analytically characterize the trade-offs between dynamic connectivity, Byzantine detection, privacy levels, and convergence speed. Leveraging these insights, we develop a fully distributed Graph Neural Network (GNN)-based Reinforcement Learning (RL) algorithm. This approach enables devices to make autonomous connection decisions based on local observations. Simulation results demonstrate that our method achieves superior robustness and efficiency with significantly lower overhead compared to traditional security and privacy schemes.

Problem

• Distributed Federated Learning
• Privacy Leakage
• Byzantine Adversaries
• Convergence Speed
• Decentralized Training

Innovation

• Bayesian Federated Learning
• Byzantine Robustness
• Privacy Preservation
• Graph Neural Network
• Reinforcement Learning