Federated Detection at the Edge: Collaborative Anomaly Detection for Resource-Limited IoT
Vasilis Ieropoulos, Eirini Anthi, Theodoros Spyridopoulos, Pete Burnap, Pietro Carnelli, Aftab Khan · IEEE Internet of Things Journal · 2026
The rapid expansion of Internet of Things (IoT) devices has heightened the need for effective intrusion detection systems (IDS) that operate under strict resource constraints. Conventional IDS implementations require substantial computational resources, making them unsuitable for low-power microcontroller-based devices. This paper proposes a novel collaborative IDS architecture that separates centralised model training from distributed edge inference. The system employs an autoencoder-based labelling mechanism trained on regular traffic to identify anomalies. Each ESP32 device performs local inference and exchanges predictions via UDP multicast, whilst MD5 hashing ensures model consistency across the network. Collaborative verification enables devices to identify and isolate compromised nodes without central coordination. Experimental evaluation demonstrates 98.5% F-score with 3ms average inference latency and 12.7KB memory footprint, consuming only 2.4% of available SRAM. Our approach achieves superior detection accuracy compared to existing cloud-edge systems whilst operating on severely resource-constrained hardware, making it a practical solution for large-scale IoT security deployments.