Decentralized Collaborative Learning Framework for Anomaly Detection with Privacy Leakage Analysis

This paper presents a decentralized collaborative learning framework that incorporates deep variational autoencoders (VAEs) for enhanced anomaly detection, while providing a theoretical analysis on external data privacy leakage when the trained models are shared.