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SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
journal contribution
posted on 2023-07-20, 06:03 authored by Chenhao Xu, Jiaqi Ge, Yong Li, Yao Deng, Longxiang GaoLongxiang Gao, Mengshi Zhang, Yong XiangYong Xiang, Xi ZhengFederated 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.
History
Journal
IEEE Transactions on Mobile ComputingVolume
PPPagination
1-14Location
Piscataway, N.J.Publisher DOI
ISSN
1536-1233eISSN
1558-0660Language
engPublication classification
C1 Refereed article in a scholarly journalIssue
99Publisher
IEEEUsage metrics
Categories
Keywords
4605 Data Management and Data Science4606 Distributed Computing and Systems Software46 Information and Computing SciencesPatient Safety4006 Communications engineering4604 Cybersecurity and privacy4606 Distributed computing and systems softwareCommunications Technologies not elsewhere classifiedElectrical and Electronic Engineering not elsewhere classifiedDistributed Computing
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