Blockchain controlled trustworthy federated learning platform for smart homes
Biswas S. Sharif K. Latif Z. Alenazi M.J.F. Pradhan A.K. Bairagi A.K.
December 2024John Wiley and Sons Inc
IET Communications
2024#18Issue 201840 - 1852 pp.
Smart device manufacturers rely on insights from smart home (SH) data to update their devices, and similarly, service providers use it for predictive maintenance. In terms of data security and privacy, combining distributed federated learning (FL) with blockchain technology is being considered to prevent single point failure and model poising attacks. However, adding blockchain to a FL environment can worsen blockchains scaling issues and create regular service interruptions at SH. This article presents a scalable Blockchain-based Privacy-preserving Federated Learning (BPFL) architecture for an SH ecosystem that integrates blockchain and FL. BPFL can automate SHs services and distribute machine learning (ML) operations to update IoT manufacturer models and scale service provider services. The architecture uses a local peer as a gateway to connect SHs to the blockchain network and safeguard user data, transactions, and ML operations. Blockchain facilitates ecosystem access management and learning. The Stanford Cars and an IoT dataset have been used as test bed experiments, taking into account the nature of data (i.e. images and numeric). The experiments show that ledger optimisation can boost scalability by 40–60% in BCN by reducing transaction overhead by 60%. Simultaneously, it increases learning capacity by 10% compared to baseline FL techniques.
blockchain , computer network security , federated learning
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Department of Computer Science, City St Georges, University of London, London, United Kingdom
School of Computer Science and Technology, Beijing Institute of Technology, Haidian District, Beijing, China
Department of Computer Science, School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
SRM University-AP, Andhra Pradesh, India
Computer Science and Engineering Discipline, Khulna University, Khulna, Bangladesh
Department of Computer Science
School of Computer Science and Technology
Department of Computer Science
Department of Computer Engineering
SRM University-AP
Computer Science and Engineering Discipline
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