Management of Resource at the Network Edge for Federated Learning

Autor:	Trindade, Silvana, Bittencourt, Luiz F., da Fonseca, Nelson L. S.
Rok vydání:	2021
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: arXiv admin note: text overlap with arXiv:1803.05255 by other authors
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.03428 Zobrazit plný text záznamu View this record from Arxiv