Federated learning has been explored as a promising solution for training at the edge, where end devices collaborate to train models without sharing data with other entities. Since the execution of these learning models occurs at the edge, where resources are limited, new solutions must be developed. In this paper, we describe the recent work on resource management at the edge, and explore the challenges and future directions to allow the execution of federated learning at the edge. Some of the problems of this management, such as discovery of resources, deployment, load balancing, migration, and energy efficiency will be discussed in the paper.

翻译：联邦学习作为边缘地区培训的一个有希望的解决方案得到了探讨,在边缘地区,终端设备合作培训模型,而没有与其他实体分享数据;由于这些学习模式的实施是在边缘地区,资源有限,因此必须开发新的解决方案;在本文件中,我们介绍最近在边缘地区资源管理方面的工作,并探讨在边缘地区开展联合学习的挑战和未来方向;这一管理的一些问题,如发现资源、部署、负载平衡、移徙和能源效率,将在文件中讨论。