Decentralized Federated Learning for Adversarial Anomaly Detection in Consumer-Grade UAV-Assisted MEC Systems

作者：Longxiang Xue, Luying Zhong, Junjie Zhang, Zheyi Chen, Hongju Cheng, Jie Li · 发表于：IEEE Transactions on Consumer Electronics · 年份：2025 · DOI：10.1109/tce.2025.3617540 · 被引用次数：2 · 研究领域：Adversarial Robustness in Machine Learning、Anomaly Detection Techniques and Applications、Network Security and Intrusion Detection

Anomaly detection is a key technique to ensure the security of consumer-grade Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) systems. While integrating emerging Decentralized Federated Learning (DFL) tends to enhance the efficiency and reduce overhead of anomaly detection, most existing DFL frameworks still face huge challenges in system and statistical heterogeneity. This vulnerability makes them highly susceptible to adversarial attacks during global communication, significantly undermining system robustness. To address these important challenges, we proposeDFL2AD, a novel DFL framework for Adversarial Anomaly Detection in consumer-grade UAV-assisted MEC systems. First, we split the local model into top-level and bottom-level components. The bottom-level model is compressed via model compression, and the UAVs only exchange the compressed bottom model with their neighbors, thereby reducing the communication overload of neighboring UAVs. Next, we design a new self-knowledge distillation mechanism. This mechanism uses the local model from the previous round as a teacher model to guide the training of the post-aggregation model in the current round, thereby preventing the forgetting of prior knowledge. Finally, we develop a residual detection strategy for identifying and replacing adversarial models. Notably, we theoretically prove the stable convergence ofDFL2ADduring the aggregation process. Based on the real-world testbed and traffic datasets, extensive e...