Wang, Z.; Liu, X.; Shao, X.; Alghamdi, A.; Alrizq, M.; Munir, M.S.; Biswas, S. An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT. Mathematics2023, 11, 4844.
Wang, Z.; Liu, X.; Shao, X.; Alghamdi, A.; Alrizq, M.; Munir, M.S.; Biswas, S. An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT. Mathematics 2023, 11, 4844.
Wang, Z.; Liu, X.; Shao, X.; Alghamdi, A.; Alrizq, M.; Munir, M.S.; Biswas, S. An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT. Mathematics2023, 11, 4844.
Wang, Z.; Liu, X.; Shao, X.; Alghamdi, A.; Alrizq, M.; Munir, M.S.; Biswas, S. An Optimized and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT. Mathematics 2023, 11, 4844.
Abstract
Consumer IoT (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) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised 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 contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.
Keywords
Blockchain; IoT; Security and Privacy; Smart Home; Distributed Ledger Technology
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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