[2603.21596] In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis

Computer Science > Machine Learning arXiv:2603.21596 (cs) [Submitted on 23 Mar 2026] Title:In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis Authors:Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel, Lei Pan, Ruby D View a PDF of the paper titled In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis, by Devashish Chaudhary and 4 other authors View PDF HTML (experimental) Abstract: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 accurat...