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.

翻译：去中心化联邦学习（DFL）仍然极易受到旨在绕过传统被动防御指标的适应性后门攻击的影响。为解决这一局限性，我们将防御范式转向一种新颖的主动干预审计框架。首先，我们建立了一个动力学模型，以刻画对抗性更新在复杂图拓扑上的时空扩散。其次，我们引入了一套主动审计度量，包括随机熵异常、随机平滑Kullback-Leibler散度和激活峰度。这些度量利用私有探针来对局部模型进行压力测试，有效暴露了传统静态检测无法察觉的潜在后门。此外，我们实现了一种拓扑感知的防御部署策略，以最大化全局聚合的鲁棒性。我们为系统在共同演化的攻防动态下的收敛性提供了理论性质。跨多种架构的数值实证评估表明，我们的主动框架在减轻隐蔽、适应性后门攻击的同时保持主任务效用方面，与最先进的防御策略高度竞争。