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Backdoors Leave Structural Traces: FedMAST for Backdoor Detection and Containment in Federated Learning

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Federated learning enables distributed training of a shared model without requiring clients to share their raw data. However, its reliance on the integrity of the client-submitted updates exposes the global model to stealthy backdoor poisoning. Although existing defenses often inspect isolated evidence sources, stealth-constrained attacks can adapt to these signals. In this paper, we show that such attacks can suppress isolated anomaly signals, but their poisoned updates still leave residual structural traces. We propose FedMAST, a Federated Multi-Axis Structural Tracing defense for backdoor d

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