doi: 10.7763/IJCTE.2026.V18.1391
Edge Federated Learning for Privacy Aware IoT Sensor Networks
Mohammad Abu Kausar1
,
Mohammad Nasar2,*
- 1Department of Information Systems, University of Nizwa, Oman
- 2Computing & Informatics Department, Mazoon College, Muscat, Oman
- Manuscript receivedJuly 25, 2025
- revisedSeptember 27, 2025
- acceptedJanuary 23, 2026
- publishedApril 17, 2026
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
How to 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. https://doi.org/10.7763/IJCTE.2026.V18.1391
Copyright & License
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).