The fifth generation (5G) and beyond wireless networks are envisioned to provide an integrated communication and computing platform that will enable multipurpose and intelligent networks driven by a growing demand for both traditional end users and industry verticals. This evolution will be realized by innovations in both core and access capabilities, mainly from virtualization technologies and ultra-dense networks, e.g., software-defined networking (SDN), network slicing, network function virtualization (NFV), multi-access edge computing (MEC), terahertz (THz) communications, etc. However, those technologies require increased complexity of resource management and large configurations of network slices. In this new milieu, with the help of artificial intelligence (AI), network operators will strive to enable AI-empowered network management by automating radio and computing resource management and orchestration processes in a data-driven manner. In this regard, most of the previous AI-empowered network management approaches adopt a traditional centralized training paradigm where diverse training data generated at network functions over distributed base stations associated with MEC servers are transferred to a central training server. On the other hand, to exploit distributed and parallel processing capabilities of distributed network entities in a fast and secure manner, federated learning (FL) has emerged as a distributed AI approach that can enable many AI-empowered network management approaches by allowing for AI training at distributed network entities without the need for data transmission to a centralized server. This article comprehensively surveys the field of FL-empowered mobile network management for 5G and beyond networks from access to the core. Specifically, we begin with an introduction to the state-of-the-art of FL by exploring and analyzing recent advances in FL in general. Then, we provide an extensive survey of AI-empowered network management, including background on 5G network functions, mobile traffic prediction, and core/access network management regarding standardization and research activities. We then present an extensive survey of FL-empowered network management by highlighting how FL is adopted in AI-empowered network management. Important lessons learned from this review of AI and FL-empowered network management are also provided. Finally, we complement this survey by discussing open issues and possible directions for future research in this important emerging area.

中文翻译：

---

联邦学习赋能的 5G 及其他网络移动网络管理：从接入到核心


第五代 (5G) 及更高版本的无线网络预计将提供一个集成通信和计算平台，在传统最终用户和垂直行业不断增长的需求的推动下，实现多功能和智能网络。这种演进将通过核心和接入能力的创新来实现，主要来自虚拟化技术和超密集网络，例如软件定义网络（SDN）、网络切片、网络功能虚拟化（NFV）、多接入边缘计算（ MEC）、太赫兹（THz）通信等。然而，这些技术需要增加资源管理的复杂性和网络切片的大型配置。在这种新环境中，在人工智能（AI）的帮助下，网络运营商将努力以数据驱动的方式自动化无线电和计算资源管理和编排流程，从而实现人工智能赋能的网络管理。在这方面，之前的大多数人工智能网络管理方法都采用传统的集中式训练模式，其中与MEC服务器相关的分布式基站上的网络功能生成的各种训练数据被传输到中央训练服务器。另一方面，为了以快速、安全的方式利用分布式网络实体的分布式并行处理能力，联邦学习（FL）作为一种分布式人工智能方法应运而生，它可以通过允许人工智能训练来实现许多人工智能支持的网络管理方法分布式网络实体，无需将数据传输到集中式服务器。本文全面调查了 FL 支持的 5G 及其他网络从接入到核心的移动网络管理领域。 具体来说，我们首先通过探索和分析 FL 的最新进展来介绍 FL 的最新技术。然后，我们对人工智能赋能的网络管理进行了广泛的调查，包括 5G 网络功能、移动流量预测以及有关标准化和研究活动的核心/接入网络管理的背景。然后，我们通过重点介绍如何在 AI 赋能的网络管理中采用 FL，对 FL 赋能的网络管理进行广泛的调查。还提供了从这次对 AI 和 FL 授权的网络管理的审查中汲取的重要经验教训。最后，我们通过讨论这一重要新兴领域的未决问题和未来研究的可能方向来补充本次调查。