Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning

• URL: http://arxiv.org/abs/2603.18538v1
• Date: Thu, 19 Mar 2026 06:46:55 GMT
• Title: Beyond Passive Aggregation: Active Auditing and Topology-Aware Defense in Decentralized Federated Learning
• Abstract summary: Decentralized Learning (DFL) remains highly vulnerable to backdoor attacks designed to bypass traditional defense metrics.<n>To address this limitation, we shift the defensive paradigm toward a novel active, interventional auditing framework.
• Score: 1.8262547855491456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address this limitation, we shift the defensive paradigm toward a novel active, interventional auditing framework. First, we establish a dynamical model to characterize the spatiotemporal diffusion of adversarial updates across complex graph topologies. Second, we introduce a suite of proactive auditing metrics, stochastic entropy anomaly, randomized smoothing Kullback-Leibler divergence, and activation kurtosis. These metrics utilize private probes to stress-test local models, effectively exposing latent backdoors that remain invisible to conventional static detection. Furthermore, we implement a topology-aware defense placement strategy to maximize global aggregation resilience. We provide theoretical property for the system's convergence under co-evolving attack and defense dynamics. Numeric empirical evaluations across diverse architectures demonstrate that our active framework is highly competitive with state-of-the-art defenses in mitigating stealthy, adaptive backdoors while preserving primary task utility.

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