Foundation models (FMs) are general-purpose artificial intelligence (AI) models that have recently enabled multiple brand-new generative AI applications. The rapid advances in FMs serve as an important contextual backdrop for the vision of next-generation wireless networks, where federated learning (FL) is a key enabler of distributed network intelligence. Currently, the exploration of the interplay between FMs and FL is still in its nascent stage. Naturally, FMs are capable of boosting the performance of FL, and FL could also leverage decentralized data and computing resources to assist in the training of FMs. However, the exceptionally high requirements that FMs have for computing resources, storage, and communication overhead would pose critical challenges to FL-enabled wireless networks. In this article, we explore the extent to which FMs are suitable for FL over wireless networks, including a broad overview of research challenges and opportunities. In particular, we discuss multiple new paradigms for realizing future intelligent networks that integrate FMs and FL. We also consolidate several broad research directions associated with these paradigms.

翻译：基础模型是通用人工智能模型，近期推动了多个全新生成式人工智能应用的发展。基础模型的快速进步为下一代无线网络愿景提供了重要背景，而联邦学习是实现分布式网络智能的关键技术。目前，对基础模型与联邦学习相互作用机理的探索仍处于初期阶段。自然而言，基础模型能够提升联邦学习性能，而联邦学习也可利用去中心化数据与计算资源辅助基础模型训练。然而，基础模型对计算资源、存储和通信开销的极高要求，将给基于联邦学习的无线网络带来关键挑战。本文深入探讨基础模型在无线网络联邦学习中的适用程度，系统综述相关研究挑战与机遇。特别地，我们讨论了融合基础模型与联邦学习的未来智能网络多种新范式，并整合了这些范式相关的若干广义研究方向。