Title: FedDefuse: Mitigating Strategic Model Poisoning for Federated Learning via Divide-and-Compute Driven Composite Behavioral Analysis
Primary area: other topics in machine learning (i.e., none of the above)
Keywords: Federated learning, model poisoning attack, non-i.i.d. data, composite behavioral pattern

Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing local data, but it is highly vulnerable to strategic model poisoning, where adversaries dominate participation rounds and may selectively launch arbitrary attacks under non-i.i.d. data. Existing defenses, often relying on single-perspective behavioral heuristics, fail to reliably distinguish and suppress malicious behaviors due to the erased distinctions between benign and malicious updates. In this paper, we propose FedDefuse, a principled  defense framework built upon a novel composite behavioral pattern that judiciously fuses two complementary indicators, intra-client recoverability and inter-client similarity, in a divide-and-compute manner. FedDefuse first divides the uploaded model updates into two candidate clusters based on their recoverability, which quantifies how faithfully each update can be reproduced through simulated local training. It then identifies benign updates as those exhibiting higher similarity scores to provisional benign clusters in the frequency domain. This design allows FedDefuse to effectively suppress adversarial contributions in the global aggregation without sacrificing benign ones. Extensive experiments demonstrate that FedDefuse significantly outperforms state-of-the-art defenses under strategic model poisoning scenarios, achieving considerable improvements in terms of both detection and model accuracy across diverse settings.