Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of wireless systems, such as sixth-generation (6G) mobile network. However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices. Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. While there are a lot of survey papers exploring FL paradigms and usability in 6G privacy, none of them has clearly addressed how FL can be used to improve the protocol stack and wireless operations. The main goal of this survey is to provide a comprehensive overview on FL usability to enhance mobile services and enable smart ecosystems to support novel use-cases. This paper examines the added-value of implementing FL throughout all levels of the protocol stack. Furthermore, it presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry and sustain the development of cutting-edge mobile services.

翻译：人工智能（AI）预计将在下一代无线系统（如第六代（6G）移动网络）中发挥关键作用。然而，无线系统中的海量数据、能耗、训练复杂度以及敏感数据保护，都是训练AI模型、从分布式设备收集智能与知识时必须应对的关键挑战。联邦学习（FL）作为一种新兴框架，为多个学习代理构建准确且鲁棒的机器学习模型而不共享原始数据提供了有前景的途径。通过允许移动终端和设备协作学习全局模型而无需明确共享训练数据，FL展现出高隐私性和高效的频谱利用率。尽管已有大量综述论文探讨了FL的范式及其在6G隐私中的可用性，但均未清晰阐述如何利用FL改进协议栈与无线操作。本综述的主要目标是全面概述FL在增强移动服务与赋能智能生态系统以支持新型用例方面的可用性。本文考察了在协议栈各层级实施FL的附加价值。此外，本文还介绍了重要的FL应用，探讨了热点话题，提供了宝贵洞见，并为未来研究方向与发展给出了明确指导。我们的总结旨在利用FL与未来6G的协同效应，同时突显FL在革新无线行业、支撑尖端移动服务持续发展方面的潜力。