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.

人工智能在下一代无线系统（如第六代（6G）移动网络）中发挥重要作用，但在训练 AI 模型和从分布式设备中获取智能和知识时，需要解决的关键挑战包括大量的数据、能源消耗、训练复杂性和敏感数据保护。联邦学习作为最近出现的框架，为多个学习代理构建准确而稳健的机器学习模型提供了有希望的方法，同时不共享原始数据。联邦学习通过允许移动设备协同学习全局模型而不显式共享训练数据，展示了高隐私性和高效的频谱利用。本文的主要目标是全面介绍联邦学习的可用性，以提高移动服务的质量，并支持支持新的用例。本文通过检查在协议堆栈的各个级别上实施联邦学习的附加价值，展示了联邦学习在改进协议堆栈和无线操作方面的重要性。此外，它给出了重要的联邦学习应用，讨论了热门话题，并为未来的研究和发展提供了宝贵的见解和指导。我们的结论旨在发挥联邦学习和未来 6G 之间的协同作用，同时强调联邦学习在颠覆无线行业和支持前沿移动服务发展中的潜力。

6G 下的联邦学习：范式、分类、最新进展与洞见

Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and  Insights

With the proliferation of distributed edge computing resources, the 6G mobile
network will evolve into a network for connected intelligence. Along this line,
the proposal to incorporate federated learning into the mobile edge has gained
considerable interest in recent years. However, the deployment of federated
learning faces substantial challenges as massive resource-limited IoT devices
can hardly support on-device model training. This leads to the emergence of
split learning (SL) which enables servers to handle the major training workload
while still enhancing data privacy. In this article, we offer a brief overview
of key advancements in SL and articulate its seamless integration with wireless
edge networks. We begin by illustrating the tailored 6G architecture to support
edge SL. Then, we examine the critical design issues for edge SL, including
innovative resource-efficient learning frameworks and resource management
strategies under a single edge server. Additionally, we expand the scope to
multi-edge scenarios, exploring multi-edge collaboration and mobility
management from a networking perspective. Finally, we discuss open problems for
edge SL, including convergence analysis, asynchronous SL and U-shaped SL.

本文主要介绍了在 6G 移动网络时代，如何将分布式边缘计算资源与联邦学习相结合，并引入分裂学习以处理海量资源受限的物联网设备，重点讨论边缘分裂学习及其在多边缘协作与移动管理中的设计问题与待解决的开放性问题。