Decentralized Learning with Federated Bayesian Networks

Abstract

This paper proposes a novel decentralized learning algorithm for Bayesian networks, termed Federated Bayesian Networks (FBNs). The core idea is to enable nodes within a network to learn independently and collaboratively, mirroring the principles of federated learning. Each node maintains its own Bayesian network and updates its parameters based on probabilistic information received from its neighbors. This approach avoids the need for centralized data aggregation, addressing key challenges associated with privacy and scalability in traditional Bayesian network learning. The algorithm iteratively refines both the network structure and its parameters, leading to a more accurate and robust global model. Mathematical formulations are presented to detail the update rules and convergence properties of the FBN algorithm. The key contribution lies in establishing a framework for distributed Bayesian network learning, particularly well-suited for scenarios with heterogeneous data and limited communication bandwidth. This work lays the foundation for applying FBNs to diverse applications, including healthcare, smart cities, and anomaly detection.