Blockchain Empowered Federated Learning Ecosystem for Securing Consumer IoT Features Analysis

Abstract

Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers’ raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users’ shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

“…This process is repeated until convergence or a predefined criterion is met. Centralised aggregation-based FL has some advantages over traditional centralised ML, such as reducing communication overhead, enhancing data privacy, and enabling distributed learning on heterogeneous devices [24][25][26][27]. However, it also faces some challenges, as follows:…”

Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems

“…Blockchain-Assisted FL: Blockchain-assisted FL represents an emerging and promising field of research in education, having already sparked interest in other domains, e.g., wireless communications (Alghamdi et al 2022;Jaberzadeh et al 2023;Billah et al 2022;Chhetri et al 2023;Salim, Turnbull, and Moustafa 2021;Li et al 2024). Combining FL's distributed learning framework with blockchain's robust, immutable ledger system can increase accountability in managing student records, improve resource sharing across institutions, and foster trust in handling student data.…”

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“…Peers: The BCN comprises multiple peers (i.e., more than three) to ensure consensus and distributed ledger management [26]. Peers receive the transactions from the GWP and verify the source and credentials for the next processing round.…”