Federated Learning Enabled Link Scheduling in D2D Wireless Networks
Federated Learning Enabled Link Scheduling in D2D Wireless Networks
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DOI：
10.1109/lwc.2023.3321500
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发表时间：
2024-01
影响因子：
6.3
通讯作者：
Tianrui Chen;Xinruo Zhang;Minglei You;Gan Zheng;S. Lambotharan
中科院分区：
文献类型：
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作者：
Tianrui Chen;Xinruo Zhang;Minglei You;Gan Zheng;S. Lambotharan
用于设备到设备（D2 D）链路调度的集中式机器学习方法可能会给中央服务器带来计算负担、决策的传输延迟以及D2 D通信的隐私问题。为了缓解这些挑战，提出了一种基于联邦学习（FL）的方法来解决链路调度问题，其中全局模型在本地设备上进行分布式训练，并且使用服务器来聚合模型参数而不是训练样本。特别地，考虑了具有有限信道状态信息（CSI）的更现实的场景，而不是完整CSI。尽管分散实施，仿真结果表明，所提出的FL为基础的方法与有限的CSI执行接近传统的优化算法。此外，基于FL的解决方案实现了与集中式训练几乎相同的性能。
Centralized machine learning methods for device-to-device (D2D) link scheduling may lead to a computing burden for a central server, transmission latency for decisions, and privacy issues for D2D communications. To mitigate these challenges, a federated learning (FL) based method is proposed to solve the link scheduling problem, where a global model is distributedly trained at local devices, and a server is used for aggregating model parameters instead of training samples. Specially, a more realistic scenario with limited channel state information (CSI) is considered instead of full CSI. Despite a decentralized implementation, simulation results demonstrate that the proposed FL based approach with limited CSI performs close to the conventional optimization algorithm. In addition, the FL based solution achieves almost the same performance as that of the centralized training.