Decentralized Knowledge Graph Construction via Bayesian Networks and Federated Learning

Abstract

This paper presents a novel approach to decentralized knowledge graph construction using Bayesian Networks and Federated Learning. The core idea is to distribute the knowledge graph construction process across a network of nodes, each maintaining a local Bayesian Network. Federated Learning is then employed to iteratively refine these local networks by aggregating anonymized data, ultimately leading to a globally consistent and accurate knowledge graph. This architecture mitigates the risks associated with centralized data silos, enhancing data privacy and improving the robustness of the knowledge representation. The proposed method leverages the probabilistic reasoning capabilities of Bayesian Networks alongside the efficiency of Federated Learning, offering a scalable and adaptive solution for knowledge graph development in distributed environments. The key contributions of this work include the integration of these two powerful techniques and the demonstration of their effectiveness in constructing decentralized knowledge graphs. Specifically, we explore the mathematical foundations underpinning the Bayesian Network representation and the Federated Learning algorithm, highlighting the convergence properties and optimization strategies involved. The resulting knowledge graph reflects a nuanced understanding of the underlying data, capturing relationships and dependencies in a robust and privacy-preserving manner.