Federated Learning-based Resource Allocation in RSU assisted Moving Network

被引：0

作者：

Zafar, Saniya ^{[1
]}

Jangsher, Sobia ^{[2
]}

Zafar, Adnan ^{[1
]}

机构：

[1] Inst Space Technol, Wireless & Signal Proc Lab, Islamabad, Pakistan

[2] Dublin City Univ, Dublin, Ireland

来源：

2024 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC 2024 | 2024年

关键词：

Deep Learning (DL); Federated Learning (FL); moving Small Cell (moSC); Resource Allocation; Roadside Unit (RSU);

D O I：

10.1109/WCNC57260.2024.10570762

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Motivated by the proliferation of smart applications and the ever accumulating concerns of data privacy, a distributed deep learning (DL) model named federated learning (FL) has been emerging. FL enables the model learning in a distributed manner without sending data from all users to a centralized hub. In this paper, we consider FL for resource allocation in roadside units (RSUs)-assisted moving network. In our proposed work, we investigate resource allocation in moving network with RSUs integrated along the roads that serve moving small cells (moSCs) deployed on trams travelling with deterministic mobility. The proposed algorithm trains the resource allocation model in a distributed manner, in which each RSU exploits its computational power and the training data of its associated moSCs to generate a shared model. We provide numerical results to validate our proposed algorithm.

引用

页数：6

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