论文标题
联合边缘学习的基于数据质量的计划
Data-Quality Based Scheduling for Federated Edge Learning
论文作者
论文摘要
Federated Edge Learning（Feel）已成为无线边缘网络中隐私分布式培训的领先技术，其中Edge设备通过服务器的编排进行了协作训练机器学习（ML）模型。但是，由于频繁的沟通，感觉需要适应有限的通信带宽。此外，本地数据集分布的统计异质性以及有关数据质量的不确定性对培训的收敛构成了重要的挑战。因此，必须精心选择参与设备和类似的带宽分配。在本文中，我们提出了一种基于数据质量的调度（DQS）算法。 DQS用丰富而多样的数据集优先考虑可靠的设备。在本文中，我们定义了学习算法的不同组成部分和数据质量评估。然后，我们制定设备选择和带宽分配问题。最后，我们介绍了DQS算法的感觉，并在不同的数据中毒方案中对其进行了评估。
FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train machine learning (ML) models with the orchestration of a server. However, due to frequent communication, FEEL needs to be adapted to the limited communication bandwidth. Furthermore, the statistical heterogeneity of local datasets' distributions, and the uncertainty about the data quality pose important challenges to the training's convergence. Therefore, a meticulous selection of the participating devices and an analogous bandwidth allocation are necessary. In this paper, we propose a data-quality based scheduling (DQS) algorithm for FEEL. DQS prioritizes reliable devices with rich and diverse datasets. In this paper, we define the different components of the learning algorithm and the data-quality evaluation. Then, we formulate the device selection and the bandwidth allocation problem. Finally, we present our DQS algorithm for FEEL, and we evaluate it in different data poisoning scenarios.
