SCEI：一种面向物联网系统的智能合约驱动的边缘智能框架
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
AI总结：
本文提出SCEI框架，结合区块链智能合约与联邦学习，通过动态优化个性化模型权重，解决非iid数据偏移和设备信任问题，实验证明其准确率和收敛速度优于传统方法。
摘要：
联邦学习（FL）能够在边缘设备上协作训练共享模型，同时保持数据隐私。联邦学习在处理独立同分布（iid）数据集时是有效的，但在面对非独立同分布（non-iid）数据集时表现不佳。人们已经提出了各种个性化方法，但这些方法无法处理数据分布中的潜在偏移，例如在现实场景中常见的数据分布倾斜（例如智能交通系统中驾驶员行为随时间和地点而变化）。此外，互不相识的设备之间的信任问题以及集中式聚合器的安全问题带来了额外的挑战。为了解决这些挑战，本文提出了一种基于区块链和联邦学习的动态优化的个性化深度学习方案。具体而言，区块链中实现的创新智能合约允许分布式边缘设备就个性化模型的最优权重达成共识。使用多个模型和真实世界数据集进行的实验评估表明，与传统的联邦学习和个性化学习方法相比，所提出的方案实现了更高的准确率和更快的收敛速度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
