论文标题
在无线网络边缘分布式机器学习的D2D启用数据共享
D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless Network Edge
论文作者
论文摘要
移动边缘学习是一种新兴技术，它可以通过利用其本地数据示例以及通信和计算资源来在培训共享的机器学习模型中协作共享机器学习模型。为了处理此技术中面临的Straggler困境问题，本文提出了一种新的设备来启用数据共享方法，其中不同的边缘设备在通信链接上相互共享他们的数据样本，以便适当调整其计算负载以提高训练速度。在此设置下，我们优化了数据共享和分布式培训的无线电资源分配，目的是最大程度地减少固定数量的本地和全球迭代次数下的总训练延迟。数值结果表明，提出的数据共享设计大大减少了训练延迟，并且当数据样本非独立且在边缘设备之间分布时，也提高了训练精度。
Mobile edge learning is an emerging technique that enables distributed edge devices to collaborate in training shared machine learning models by exploiting their local data samples and communication and computation resources. To deal with the straggler dilemma issue faced in this technique, this paper proposes a new device to device enabled data sharing approach, in which different edge devices share their data samples among each other over communication links, in order to properly adjust their computation loads for increasing the training speed. Under this setup, we optimize the radio resource allocation for both data sharing and distributed training, with the objective of minimizing the total training delay under fixed numbers of local and global iterations. Numerical results show that the proposed data sharing design significantly reduces the training delay, and also enhances the training accuracy when the data samples are non independent and identically distributed among edge devices.
