FEDERATED LEARNING FOR DISTRIBUTED NETWORK TRAFFIC ANOMALY DETECTION USING GRU MODELS

Abstract

This work introduces a novel GRU-based federated learning approach for anomaly detection in network traffic. Our decentralized method effectively addresses privacy concerns and constraints that prevent centralized servers. Supported by extensive experiments and comparisons, it demonstrates strong performance in detecting anomalies across distributed environments.