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.

本研究针对联邦边缘学习（FEL）中，传统资源分配方案无法满足复杂应用场景下越来越高的计算和通信需求的问题，提出了联合资源优化的新思路。通过系统回顾FEL中不同资源的联合分配策略，研究显示这种方法能有效提升系统效率、减少延迟和增强资源利用率，同时有助于隐私保护。这项工作为联邦学习系统的资源管理提供了理论支持，并为实际应用场景的最优部署提供了创新思路。

联邦边缘学习中联合资源分配策略的全面调查