QuAsyncFL: Asynchronous Federated Learning With Quantization for CloudEdgeTerminal Collaboration Enabled AIoT

文献类型： 外文期刊

第一作者： Liu, Ye

作者： Liu, Ye;Liu, Ye;Huang, Peishan;Yang, Fan;Huang, Kai;Shu, Lei;Shu, Lei

作者机构：

关键词： Artificial Intelligence of Things (AIoT); asynchronous federated learning; cloud-edge-terminal collaboration; communication efficiency; quantization

期刊名称：IEEE INTERNET OF THINGS JOURNAL （ 影响因子：8.2； 五年影响因子：9.0 ）

ISSN： 2327-4662

年卷期： 2024 年 11 卷 1 期

页码：

收录情况： SCI

摘要： Federated Learning is a promising technique that facilitates cloud-edge-terminal collaboration in Artificial Intelligence of Things (AIoT). It will enable model training without centralizing data, addressing privacy, and security concerns. However, when applied to AIoT, this technique faces several challenges, such as low communication efficiency among terminal devices, edges, and cloud platforms. In this article, we propose a novel approach called asynchronous federated learning with quantization (QuAsyncFL), which combines asynchronous federated learning with an unbiased nonuniform quantizer to address the issue of low communication efficiency. Moreover, we provide a detailed theoretical analysis of convergence with quantized gradients proving that the model could converge to a certain bound. Our experiments demonstrate that QuAsyncFL outperforms the original approach, achieving significant improvements in terms of communication efficiency. The research results represent a further step toward developing cloud-edge-terminal collaboration enabled AIoT.

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