Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models exhibit exceptional performance across diverse downstream tasks through adaptation techniques like fine-tuning and prompt learning. More recently, the synergy of FMs and Federated Learning (FL) has emerged as a promising paradigm, often termed Federated Foundation Models (FedFM), allowing for collaborative model adaptation while preserving data privacy. This survey paper provides a systematic review of the current state of the art in FedFM, offering insights and guidance into the evolving landscape. Specifically, we present a comprehensive multi-tiered taxonomy based on three major dimensions, namely efficiency, adaptability, and trustworthiness. To facilitate practical implementation and experimental research, we undertake a thorough review of existing libraries and benchmarks. Furthermore, we discuss the diverse real-world applications of this paradigm across multiple domains. Finally, we outline promising research directions to foster future advancements in FedFM. Overall, this survey serves as a resource for researchers and practitioners, offering a thorough understanding of FedFM's role in revolutionizing privacy-preserving AI and pointing toward future innovations in this promising area. A periodically updated paper collection on FM-FL is available at https://github.com/lishenghui/awesome-fm-fl.

翻译：过去几年，人工智能（AI）领域因基础模型（FMs）的出现而重塑。这些模型在海量数据集上预训练，通过微调和提示学习等适应技术，在多种下游任务中展现出卓越性能。近年来，FMs与联邦学习（FL）的协同已成为一种有前景的范式，常被称为联邦基础模型（FedFM），它能够在保护数据隐私的同时实现协作式模型适应。本综述论文系统回顾了FedFM的当前最新进展，为这一不断演进的领域提供见解与指导。具体而言，我们基于效率、适应性和可信度三大维度，提出了一个全面的多层次分类体系。为促进实际实施与实验研究，我们对现有库和基准测试进行了全面梳理。此外，我们讨论了该范式在多个领域的多样化实际应用。最后，我们概述了推动FedFM未来发展的有前景的研究方向。总体而言，本综述为研究者和实践者提供了一份资源，深入阐释了FedFM在革新隐私保护AI中的作用，并指明了这一前景广阔领域的未来创新方向。关于FM-FL的定期更新论文合集可在 https://github.com/lishenghui/awesome-fm-fl 获取。