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.

中文翻译：

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联邦学习在具有基础模型的无线世界中的作用


基础模型 (FM) 是通用人工智能 (AI) 模型，最近启用了多种全新的生成式 AI 应用。 FM 的快速发展为下一代无线网络的愿景提供了重要的背景背景，其中联邦学习 (FL) 是分布式网络智能的关键推动者。目前，对 FM 和 FL 之间相互作用的探索仍处于起步阶段。当然，FM 能够提升 FL 的性能，而 FL 也可以利用去中心化的数据和计算资源来协助 FM 的训练。然而，FM 对计算资源、存储和通信开销的极高要求将为支持 FL 的无线网络带来严峻挑战。在本文中，我们探讨了 FM 在多大程度上适合无线网络上的 FL，包括对研究挑战和机遇的广泛概述。特别是，我们讨论了用于实现集成 FM 和 FL 的未来智能网络的多种新范例。我们还整合了与这些范式相关的几个广泛的研究方向。