Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis

arXiv:2403.10000v1 Announce Type: new
Abstract: In the evolving landscape of Federated Learning (FL), the challenge of ensuring data integrity against poisoning attacks is paramount, particularly for applications demanding stringent privacy preservation. Traditional anomaly detection strategies often struggle to adapt to the distributed nature of FL, leaving a gap our research aims to bridge. We introduce a novel framework that synergizes gradient-based analysis with autoencoder-driven data reconstruction to detect and mitigate poisoned data with unprecedented precision. Our approach uniquely combines detecting …

analysis anomaly detection applications arxiv attacks challenge cs.cr data data integrity detection distributed federated federated learning gap integrity nature paramount poisoning poisoning attacks preservation privacy research strategies