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.
翻译:远距离边缘学习(FEEL)已成为在无线边缘网络中进行隐私保护分散培训的领先技术,在无线边缘网络中,边缘装置协同培训机器学习模型,同时配合服务器的调控。然而,由于通信频繁,需要使感觉适应有限的通信带宽。此外,当地数据集分布的统计多样性和数据质量的不确定性对培训的趋同提出了重大挑战。因此,必须仔细选择参与的装置和类似的带宽分配。在本文件中,我们提出了基于数据质量的社交算法(DQS)。DQS优先考虑使用丰富和多样数据集的可靠装置。在本文件中,我们界定了学习算法和数据质量评价的不同组成部分。然后,我们制定了设备选择和带宽分配问题。最后,我们提出了用于感觉的DQS算法,并在不同的数据中毒假设中对其进行评估。