Addressing Data Privacy Concerns in IoT Architecture with Federated Learning and TinyML

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.