A carregar...
Publicação
Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 408.72 KB | Adobe PDF |
Orientador(es)
Resumo(s)
Mobile edge computing (MEC) has been considered
as a promising technology to provide seamless integration of
multiple application services. Federated learning (FL) is carried
out at edge clients in MEC for privacy-preserving training of
data processing models. Despite that the edge clients with small
data payloads consume less energy on FL training, the small data
payload gives rise to a low learning accuracy due to insufficient
input to the FL training. Inadequate selection of the edge clients
can result in a large energy consumption at the edge clients,
or a low learning accuracy of the FL training. In this paper,
a new FL-based client selection optimization is proposed to
balance the trade-off between energy consumption of the edge
clients and the learning accuracy of FL. We first show that
this optimization problem is NP-complete. Next, we propose
a FL-based energy-accuracy balancing heuristic algorithm to
approximate the optimal client selection in polynomial time. The
numerical results show the advantage of our proposed algorithm.
Descrição
Palavras-chave
Client selection Mobile edge computing Federated learning Heuristic algorithm
Contexto Educativo
Citação
Editora
IEEE