A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning

TL;DR

This survey reviews joint resource allocation strategies in Federated Edge Learning, highlighting their role in improving efficiency, reducing latency, and supporting privacy in complex IoT scenarios.

Contribution

It systematically summarizes existing resource optimization methods in FEL and discusses future research directions for multi-resource management.

Findings

Joint resource optimization enhances system efficiency and robustness.

Reducing communication can indirectly improve privacy preservation.

The survey identifies key challenges and potential solutions in FEL resource management.

Abstract

Federated Edge Learning (FEL), an emerging distributed Machine Learning (ML) paradigm, enables model training in a distributed environment while ensuring user privacy by using physical separation for each user data. However, with the development of complex application scenarios such as the Internet of Things (IoT) and Smart Earth, the conventional resource allocation schemes can no longer effectively support these growing computational and communication demands. Therefore, joint resource optimization may be the key solution to the scaling problem. This paper simultaneously addresses the multifaceted challenges of computation and communication, with the growing multiple resource demands. We systematically review the joint allocation strategies for different resources (computation, data, communication, and network topology) in FEL, and summarize the advantages in improving system efficiency, reducing latency, enhancing resource utilization and enhancing robustness. In addition, we present the potential ability of joint optimization to enhance privacy preservation by reducing communication requirements, indirectly. This work not only provides theoretical support for resource management in federated learning (FL) systems, but also provides ideas for potential optimal deployment in multiple real-world scenarios. By thoroughly discussing the current challenges and future research directions, it also provides some important insights into multi-resource optimization in complex application environments.