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SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
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DOI：10.1109/TMC.2023.3290925.png)
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摘要
En 中文
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.
Keyword:
Peer-to-peer computing
Data models
Training
Blockchains
Smart contracts
Computational modeling
Mobile computing
Blockchain
deep learning
distributed learning
federated learning
IoT
personalized model
smart contract
期刊
IF:
9.2
论文数:
5.8K
被引数:
1.8W
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