arXiv:2609.27760v1 Announce Type: new

Abstract: 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 detection in federated learning. FedMAST scores client updates using complementary structural, spectral, and historical evidence and then applies tiered filtering and round-level containment to limit adversarial influence. To capture traces that isolated signals may miss, FedMAST uses squeeze-pair coherence scoring to expose coupled feature distortions and signed spectral-drift tracking to reveal persistent directional changes over time. Across six federated backdoor attacks, namely Constrain-and-Scale, Neurotoxin, BC-Layers, LGA, DBA, and 3DFed, FedMAST achieves lower ASR than baseline defenses in all nine evaluated attack--defense comparisons. Across the complete 200-round runs, it attains an average ASR of 1.51% while maintaining 94.84% average main-task accuracy. Under the method-aware CovertLayers attack, FedAvg, MultiKrum, AlignIns, and FLAME yield full-run ASRs of 100.00%, 99.67%, 99.53%, and 32.84%, respectively. FedMAST achieves the lowest ASR among all evaluated methods, reducing it to 1.53% while maintaining 92.26% main-task accuracy.