Resource management at the network edge for federated learning

Autor(es)

Trindade, Silvana ; Bittencourt, Luiz F. ; da Fonseca, Nelson L. S.

• Tipo de documento: Artigo Científico
• Fonte: DIGITAL COMMUNICATIONS AND NETWORKS; v. 10, n. 3, p. 18-pg., 2024-06-28.

Resumo

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 management 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 efficiency are discussed in the paper. (AU)

• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
• Modalidade de apoio: Auxílio à Pesquisa - Temático