论文标题
联合边缘学习：设计问题和挑战
Federated Edge Learning : Design Issues and Challenges
论文作者
论文摘要
联合学习（FL）是一种分布式机器学习技术，每个设备通过基于本地培训数据独立计算梯度来为学习模型做出贡献。它最近已成为一个热门的研究主题，因为它承诺与数据隐私和可扩展性有关。但是，由于系统和数据异质性和资源约束，在网络边缘实施FL是具有挑战性的。在本文中，我们研究了联邦边缘学习（Feel）中现有的挑战和权衡。用于资源有效学习的感觉算法的设计引起了一些挑战。这些挑战基本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，因此本文提倡一套新的注意事项，以了解无线调度算法中的数据特征。因此，我们为数据感知计划的一般框架提出了一个通用框架，以作为未来研究方向的指南。我们还讨论了数据评估以及一些可利用的技术和指标的主轴和要求。
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.
