Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges. Issue 108 (September 2021)

• Record Type: Journal Article
• Authors: Ali, Mansoor; Karimipour, Hadis; Tariq, Muhammad
• Is Part Of: Computers & security. Issue 108(2021)
• Publication Date: 2021-09
• Subjects: Federated learning -- The Internet of Things -- BLockchains -- Privacy -- Dispersed federated learning; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2021.102355
• Languages: English
• ISSNs: 0167-4048

Abstract

Abstract: The role of the Internet of Things (IoT) in the revolutionized society cannot be overlooked. The IoT can leverage advanced machine learning (ML) algorithms for its applications. However, given the fact of massive data, which is stored at a central cloud server, adopting centralized machine learning algorithms is not a viable option due to immense computation cost and privacy leakage issues. Given such conditions, blockchain can be leveraged to enhance the privacy of IoT networks by making them decentralized without any central authority. Nevertheless, the sensitive and massive data that is stored in distributive fashion, leveraged it for application purpose, is still a challenging task. To overcome this challenging task, federated learning (FL), which is a new breed of ML is the most promising solution that brings learning to the end devices without sharing the private data to the central server. In the FL mechanism, the central server act as an orchestrator to start the FL learning process, and only model parameters' updates are shared between end devices and the central orchestrator. Although FL can provide better privacy and data management, it is still in the development phase and has not been adopted by various communities due to its unknown privacy issues. In this paper first, we present the notion of blockchain and its application in IoT systems. Then we describe the privacy issues related to the implementation of blockchain in IoT and present privacy preservation techniques to cope with the privacy issues. Second, we introduce the FL application in IoT systems, devise a taxonomy, and present privacy threats in FL. Afterward, we present IoT-based use cases on envisioned dispersed federated learning and introduce blockchain-based traceability functions to improve privacy. Finally, open research gaps are addressed for future work.