Exploring Deep Federated Learning for the Internet of Things: A GDPR-Compliant Architecture
Zahra Abbas, Sunila Fatima Ahmad, Madiha Haider Syed, Adeel Anjum, Semeen Rehman
Abstract
With the emergence of intelligent services and applications powered by artificial intelligence (AI), the Internet of Things (IoT) affects many aspects of our daily lives. Traditional approaches to machine learning (ML) relied on centralized data collection and processing, where data was collected and analyzed in one place. However, with the development of Deep Federated Learning (DFL), models can now be trained on decentralized data, reducing the need for centralized data storage and processing. In this work, we provide a detailed analysis of DFL and its benefits, followed by an extensive survey of the use of DFL in various IoT services and applications. We have studied the impact of DFL and how to preserve security and privacy by ensuring compliance in machine learning-enabled IoT systems. In addition, we present a generic architecture for a GDPR-compliant DFL-based framework. Finally, we discuss the existing obstacles and possible future research directions for DFL in IoT.