International Journal of Computer Theory and Engineering

Abstract—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.

Keywords—federated learning, edge computing, Internet of Things (IoT), sensor networks, privacy preservation, data security

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Cite: Mohammad Abu Kausar and Mohammad Nasar, "Edge Federated Learning for Privacy Aware IoT Sensor Networks," International Journal of Computer Theory and Engineering, vol. 18, no. 2, pp. 88-98, 2026.

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).