4.7 Article

Federated Learning With NOMA Assisted by Multiple Intelligent Reflecting Surfaces: Latency Minimizing Optimization and Auction

期刊

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷 72, 期 9, 页码 11558-11574

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2023.3264202

关键词

Auction; federated learning; intelligent reflecting surfaces; non-orthogonal multiple access; device selection

向作者/读者索取更多资源

This study proposes a method to implement federated learning using intelligent reflecting surfaces-assisted non-orthogonal multiple access technology to reduce training latency and improve training efficiency. Furthermore, an auction-based intelligent reflecting surface allocation scheme is introduced to maximize social welfare.
Federated learning (FL) has emerged as a promising framework to exploit massive data generated by edge devices in developing a common learning model while preserving the privacy of local data. In implementing FL over wireless networks, the participation of more devices is encouraged to alleviate the training inefficiency due to irregular local data but it tends to increase communication latency. To solve this problem, we address non-orthogonal multiple access (NOMA) assisted by intelligent reflecting surfaces (IRSs) to accommodate more devices and tailor their channels favorably to the FL performance. For the FL with IRS-NOMA, we minimize the total latency by reducing the latency per training round dominated by local computation and uplink communication through optimization of IRS-NOMA strategies and improving the training efficiency under irregular local data through active device selection. We then propose an auction-based IRS allocation that utilizes the optimized total latency for the valuation of the IRSs when multiple base stations of different operators share their neighboring IRSs. Winner determination (WD) and payment methods are devised with multiple bids on IRS subsets in a way of maximizing social welfare. The results show that the proposed latency minimizing algorithm outperforms the benchmarks by improving both communication and training efficiency through device selection combined with IRS-NOMA optimization. In addition, the auction mechanism with the proposed WD outperforms the benchmarks, where the social welfare is improved by constructing each bid with the valuation on multiple IRSs and increasing the number of bids submitted.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据