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（FMs）是通用人工智能（AI）模型，近期催生了多种全新的生成式AI应用。FMs的快速发展为下一代无线网络愿景提供了重要背景——在该网络中，联邦学习（FL）是实现分布式网络智能的关键技术。目前，对FMs与FL相互作用机制的探索仍处于初级阶段。自然而言，FMs能够提升FL性能，而FL也可利用分散的数据和计算资源辅助FMs训练。然而，FMs对计算资源、存储和通信开销的超高要求将给基于FL的无线网络带来关键挑战。本文探讨了FMs对无线网络联邦学习的适用性程度，系统梳理了相关研究挑战与机遇。特别地，我们讨论了融合FMs与FL的多种新型智能网络实现范式，并整合了对应范式的若干广泛研究方向。