An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT

Zhaocheng Wang, Xueying Liu (), Xinming Shao, Abdullah Alghamdi, Mesfer Alrizq, Md. Shirajum Munir and Sujit Biswas ()
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Zhaocheng Wang: School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450001, China
Xueying Liu: Cabin Attendant College, Civil Aviation University of China, Tianjin 300300, China
Xinming Shao: Computer Science and Technology Department, Zhengzhou Railway Vocational and Technical College, Zhengzhou 451460, China
Abdullah Alghamdi: Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
Mesfer Alrizq: Information Systems Department, College of Computer Science and Information Systems, Najran University, Najran 55461, Saudi Arabia
Md. Shirajum Munir: School of Cybersecurity, Old Dominion University, Norfolk, VA 23529, USA
Sujit Biswas: Computer Science and Digital Technologies Department, University of East London, University Way, London E16 2RD, UK

Mathematics, 2023, vol. 11, issue 23, 1-16

Abstract: Consumer Internet of Things (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem, like a smart home. Due to security and privacy concerns, blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their machine learning (ML) models using end-user data. Federated learning (FL) uses privacy-preserving ML techniques to forecast customers’ needs and consumption habits, and blockchain replaces the centralized aggregator to safeguard the ecosystem. However, blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research introduces a novel architecture, emphasizing gateway peer (GWP) in the blockchain network to address scalability, ledger optimization, and secure model transmission issues. In the architecture, we replace the centralized aggregator with the blockchain network, while GWP limits the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we incorporated differential privacy and advanced normalization techniques into ML processes. These approaches enhance the cybersecurity of end-users and promote the adoption of technological innovation standards by service providers. The proposed approach has undergone extensive testing using the well-respected Stanford (CARS) dataset. We experimentally demonstrate that the proposed architecture enhances network scalability and significantly optimizes the ledger. In addition, the normalization technique outperforms batch normalization when features are under DP protection.

Keywords: blockchain; IoT; security and privacy; smart home; distributed ledger technology (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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