Federated Learning and Blockchain Integration for Privacy Protection in the Internet of Things: Challenges and Solutions

Abstract

The Internet of Things (IoT) compromises multiple devices connected via a network to perform numerous activities. The large amounts of raw user data handled by IoT operations have driven researchers and developers to provide guards against any malicious threats. Blockchain is a technology that can give connected nodes means of security, transparency, and distribution. IoT devices could guarantee data centralization and availability with shared ledger technology. Federated learning (FL) is a new type of decentralized machine learning (DML) where clients collaborate to train a model and share it privately with an aggregator node. The integration of Blockchain and FL enabled researchers to apply numerous techniques to hide the shared training parameters and protect their privacy. This study explores the application of this integration in different IoT environments, collectively referred to as the Internet of X (IoX). In this paper, we present a state-of-the-art review of federated learning and Blockchain and how they have been used in collaboration in the IoT ecosystem. We also review the existing security and privacy challenges that face the integration of federated learning and Blockchain in the distributed IoT environment. Furthermore, we discuss existing solutions for security and privacy by categorizing them based on the nature of the privacy-preservation mechanism. We believe that our paper will serve as a key reference for researchers interested in improving solutions based on mixing Blockchain and federated learning in the IoT environment while preserving privacy.

A Systematic Review on Privacy-Aware IoT Personal Data Stores

“…Year Systematic Domain Main Goal Ogonji et al [15] ✓ IoT Privacy and security threats, attack surface, vulnerabilities, and countermeasures in IoT. Sarwar et al [25] x IoT and Fog Privacy preservation in IoT and fog-enabled IoT Zainuddin et al [16] x IoT Applications Privacy threats in IoT applications Islam et al [17] ✓ IoT and Big Data Types of privacy attacks and solutions in the IoT and Big Data contexts Zarandi et al [20] x Smart Farming Big Data privacy in smart farming Hireche et al [21] x IoMT Privacy and security in IoMT Shahid et al [23] x IoHT Data breaches and legal regulations in IoHT Alabdulatif et al [24] x IoNT Overview of IoNT and its security and privacy issues Torre et al [18] ✓ IoT Devices Privacy-preservation techniques for IoT devices Tokas et al [26] x IoT Privacy-enhancing technologies and GDPR-specific privacy principles Demertzi et al [27] x IIoT Privacy requirements in the Industrial IoT Fallatah et al [28] x PDS PDS and its advantages and disadvantages Zubaydi et al [29] ✓ IoT and Blockchain Blockchain solutions for IoT security and privacy issues Asqah and Moulahi [30] x…”

“…Finally, they presented solutions to address privacy and security issues, followed by categorizing investigated applications based on various characteristics such as primary information, objectives, development level, target application, blockchain type, platform, etc. In turn, Asqah and Moulahi [30] presented a review of federated learning and blockchain integration within the IoT ecosystem. They reviewed the security and privacy challenges arising from this integration and discussed and categorized solutions for these concerns.…”