Management of Resource at the Network Edge for Federated Learning

TL;DR

This paper reviews recent approaches to managing limited resources at the network edge to enable effective federated learning, addressing challenges like resource discovery, deployment, load balancing, migration, and energy efficiency.

Contribution

It provides a comprehensive overview of current resource management strategies for federated learning at the edge and discusses future research directions.

Findings

Identifies key challenges in resource management for edge federated learning.

Highlights the importance of energy efficiency and load balancing.

Discusses potential solutions for resource discovery and migration.

Abstract

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.