Federated Learning (FL) is a distributed machine learning technique, where each device contributes to the learning model by independently computing the gradient based on its local training data. It has recently become a hot research topic, as it promises several benefits related to data privacy and scalability. However, implementing FL at the network edge is challenging due to system and data heterogeneity and resources constraints. In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL). The design of FEEL algorithms for resources-efficient learning raises several challenges. These challenges are essentially related to the multidisciplinary nature of the problem. As the data is the key component of the learning, this article advocates a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. Hence, we propose a general framework for the data-aware scheduling as a guideline for future research directions. We also discuss the main axes and requirements for data evaluation and some exploitable techniques and metrics.
翻译:联邦学习(FL)是一种分布式的机器学习技术,每个设备都通过根据当地培训数据独立计算梯度,对学习模式作出贡献;最近,它已成为一个热门的研究课题,因为它承诺在数据隐私和可扩缩性方面带来若干好处;然而,由于系统和数据差异性及资源限制,在网络边缘实施FL具有挑战性;在本篇文章中,我们审查了联邦学习(FEEL)的现有挑战和取舍;设计资源节约学习的感知算法提出了几项挑战;这些挑战基本上与问题的多学科性质有关;由于数据是学习的关键组成部分,因此本文章主张在无线调度算法中采用一套新的数据特征考虑因素;因此,我们提出了数据兼容性列表总框架,作为未来研究方向的指导方针;我们还讨论了数据评价的主要轴和要求以及一些可开发的技术和计量标准。