Edge Federated Learning for Privacy Aware IoT Sensor Networks

Privacy issues and communication overhead are bottlenecks to the machine learning implementation in IoT sensor network. In this paper, we introduce a lightweight Edge-enabled Federated Learning (EFL) framework utilizing Personalized Federated Learning (FedPer) over edge computing for privacy-preserving collaborative training. Our solution mitigates the challenges of non-independent and identically distributed (non-IID) data, using client-specific personalization and secure aggregation without raw data exchange. Extensive experiments over five real-world IoT datasets (UCI HAR, Ambient, DOO-RE, SHL and WISDM) show that FedPer can achieve up to 96% accuracy—surpassing baseline methods (FedAvg, FedProx) by 2–12% within non-IID scenarios—meanwhile decreasing communication overhead by up to 20%. The hardware evaluation on Raspberry Pi 4 and Jetson Nano validates the realization of real-time inference (<30 ms/sample) for Decision Tree (DT) with compact model sizes (<500 KB). Our system offers a large-scale, privacy-assured way of intelligent sensing in smart home, wearables and industrial IoT.