Dual-side Control for Coordinated Attack Detection in Federated Learning

Abstract

Federated Learning (FL) enables collaborative model training while preserving data privacy. However, it remains highly vulnerable to poisoning attacks, particularly coordinated label- flipping attacks. In this paper, we propose DualFed, a dual-side defense framework for detecting coordinated poisoning behaviors in FL. DualFed combines client-side adaptive behavioral anomaly detection with a server-side Collective Behavioral Correlation (CBC) mechanism. On the client side, an Adaptive Adversarially Robust Statistics (AARS) mechanism integrates trimmed estimation with Exponential Moving Average (EMA) and Exponential Moving Variance (EMV) to robustly quantify abnormal performance degradation. On the server side, CBC aggregates client anomaly reports over a sliding temporal window to detect coordinated attacks. Once an attack is confirmed, DualFed activates a non-punitive self-recovery mechanism. Under non-IID settings, DualFed achieves benign accuracies exceeding 84.0MNIST, 52.0dataset, while reducing malicious attack accuracy to near zero and outperforming recent defense baselines.