The rapid expansion of the Internet of Things (IoT) and its integration with backbone networks have heightened the risk of security breaches. Traditional centralized approaches to anomaly detection, which require transferring large volumes of data to central servers, suffer from privacy, scalability, and latency limitations. This paper proposes a lightweight autoencoder-based anomaly detection framework designed for deployment on resource-constrained edge devices, enabling real-time detection while minimizing data transfer and preserving privacy. Federated learning is employed to train models collaboratively across distributed devices, where local training occurs on edge nodes and only model weights are aggregated at a central server. A real-world IoT testbed using Raspberry Pi sensor nodes was developed to collect normal and attack traffic data. The proposed federated anomaly detection system, implemented and evaluated on the testbed, demonstrates its effectiveness in accurately identifying network attacks. The communication overhead was reduced significantly while achieving comparable performance to the centralized method.

翻译：物联网的迅速扩张及其与骨干网络的融合使得安全漏洞风险日益加剧。传统的集中式异常检测方法需要将海量数据传送至中央服务器，因而面临隐私性、可扩展性和延迟方面的局限。本文提出一种轻量级基于自编码器的异常检测框架，专为资源受限的边缘设备设计，能够在减少数据传输并保护隐私的同时实现实时检测。采用联邦学习在分布式设备间协同训练模型，即在边缘节点进行本地训练，仅将模型权重聚合至中央服务器。基于树莓派传感器节点构建了真实的物联网测试平台，用于采集正常流量与攻击流量数据。所提出的联邦异常检测系统在该测试平台上实现并评估，证明其能有效识别网络攻击。通信开销显著降低，同时性能与集中式方法相当。