SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
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Abstract (arXiv)
Federated learning (FL) enables collaborative training of a shared model on edge devices while maintaining data privacy. FL is effective when dealing with independent and identically distributed (iid) datasets, but struggles with non-iid datasets. Various personalized approaches have been proposed, but such approaches fail to handle underlying shifts in data distribution, such as data distribution skew commonly observed in real-world scenarios (e.g., driver behavior in smart transportation systems changing across time and location). Additionally, trust concerns among unacquainted devices and security concerns with the centralized aggregator pose additional challenges. To address these challenges, this paper presents a dynamically optimized personal deep learning scheme based on blockchain and federated learning. Specifically, the innovative smart contract implemented in the blockchain allows distributed edge devices to reach a consensus on the optimal weights of personalized models. Experimental evaluations using multiple models and real-world datasets demonstrate that the proposed scheme achieves higher accuracy and faster convergence compared to traditional federated and personalized learning approaches.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 66 references that are in the map. First posted on arXiv 12 Mar 2021.
Builds on
25 of 66 references.
References · 25
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13 more
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Citing papers per year: 2022: 1.
1 paper.