Research Area:  Machine Learning

Abstract:

The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning algorithms are increasingly integrated into IoT applications to learn and infer patterns and make intelligent decisions. However, current IoT paradigms rely on centralised storage and computing to operate the deep learning algorithms. This key central component can potentially cause issues in scalability, security threats and privacy breaches. Federated learning (FL) has emerged as a new paradigm for deep learning algorithms to preserve data privacy. Although FL helps reduce privacy leakage by avoiding transferring client data, it still has many challenges related to models’ vulnerabilities and attacks. With the emergence of blockchain and smart contracts, the utilisation of these technologies has the potential to safeguard FL across IoT ecosystems. This study aims to review blockchain-based FL methods for securing IoT systems holistically. It presents the current state of research in blockchain, how it can be applied to FL approaches, current IoT security issues and responses to outline the need to use emerging approaches toward the security and privacy of IoT ecosystems. It also focuses on IoT data analytics from a security perspective and the open research questions. It also provides a thorough literature review of blockchain-based FL approaches for IoT applications. Finally, the challenges and risks associated with integrating blockchain and FL in IoT are discussed to be considered in future works.

Keywords:  
Blockchain
Federated Learning
Securing Internet Of Things
Deep Learning
Machine Learning

Author(s) Name:  Wael Issa , Nour Moustafa , Benjamin Turnbull , Nasrin Sohrabi , Zahir Tari

Publisher name:  ACM

DOI:  10.1145/3560816

Volume Information:  

Paper Link:   https://dl.acm.org/doi/abs/10.1145/3560816