International Journal of Computer Network and Information Security (IJCNIS)
IJCNIS Vol. 18, No. 2, 8 Apr. 2026
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Addressing Data Privacy Concerns in IoT Architecture with Federated Learning and TinyML
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Author(s)
Hiba Kandil
1,*
Hafssa Benaboud
1
Index Terms
Internet of Things (IoT), Data Privacy, Machine Learning, Federated Learning, TinyML
Abstract
The rapid extension of the Internet of Things (IoT) has introduced significant concerns, particularly in ensuring data security and safeguarding sensitive and private data. The integration of Federated Learning into IoT architecture has occurred as a covenanting solution to address the risks of data breaches, resource efficiency, and the challenges of data privacy and security. This paper presents a novel lightweight framework tailored for resource-constrained IoT devices that integrates Federated Learning and Tiny Machine Learning (TinyML) to deploy lightweight, reliable models on edge devices. Our experimental results show that the proposed approach can improve efficiency, reduce communication overhead, and enhance privacy preservation.
Cite This Paper
Hiba Kandil, Hafssa Benaboud, "Addressing Data Privacy Concerns in IoT Architecture with Federated Learning and TinyML", International Journal of Computer Network and Information Security(IJCNIS), Vol.18, No.2, pp.196-209, 2026. DOI:10.5815/ijcnis.2026.02.11
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