Securing Federated Learning Against Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection on Local Updates

Abstract

Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients (e.g., 25% to 50%). In this work, we introduce FLANDERS, a novel pre-aggregation filter for FL that is resilient to large-scale model poisoning attacks, i.e., when malicious clients far exceed legitimate participants. FLANDERS treats the sequence of local models sent by clients in each FL round as a matrix-valued time series. Then, it identifies malicious client updates as outliers in this time series by comparing actual observations with estimates generated by a matrix autoregressive forecasting model maintained by the server. Experiments conducted in several non-iid FL setups show that FLANDERS significantly improves robustness across a wide spectrum of attacks when paired with standard and robust aggregation methods.