Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2303.16668v2
• Date: Mon, 27 May 2024 09:30:37 GMT
• Title: Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection
• Authors: Edoardo Gabrielli, Dimitri Belli, Vittorio Miori, Gabriele Tolomei,
• Abstract summary: We introduce FLANDERS, a novel pre-aggregation filter for FL resilient to large-scale model poisoning attacks. 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 existing aggregation methods.
• Score: 1.74243547444997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current defense mechanisms against model poisoning attacks in federated learning (FL) systems have proven effective up to a certain threshold of malicious clients. In this work, we introduce FLANDERS, a novel pre-aggregation filter for FL 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 existing aggregation methods.

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