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Resource management at the network edge for federated learning

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Federated learning has been explored as a promising solution for training machine learning models at the network edge,without sharing private user data.With limited resources at the edge,new solutions must be developed to leverage the software and hardware resources as the existing solutions did not focus on resource management for network edge,specially for federated learning.In this paper,we describe the recent work on resource manage-ment at the edge and explore the challenges and future directions to allow the execution of federated learning at the edge.Problems such as the discovery of resources,deployment,load balancing,migration,and energy effi-ciency are discussed in the paper.

Resource managementEdge computingFederated learningMachine learning

Silvana Trindade、Luiz F.Bittencourt、Nelson L.S.da Fonseca

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Institute of Computing,State University of Campinas,Campinas,Brazil

CAPESCNPqS?o Paulo Research Foundation(FAPESP)

15/24494-8

2024

数字通信与网络(英文)

数字通信与网络(英文)

ISSN:
年,卷(期):2024.10(3)