The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of foundation models. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, the paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL, highlighting the potential of quantum computing to revolutionize the training, inference, optimization and data encryption processes. This survey underscores the importance of further research to propel innovation in FedFM, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field.

翻译：基础模型（FM）与联邦学习（FL）的融合催生了人工智能领域的变革性范式，在提升能力的同时解决了隐私保护、数据去中心化及计算效率等关键问题。本文全面综述了联邦基础模型（FedFM）这一新兴领域，阐明了二者的协同关系，并深入探讨了FL研究领域在基础模型时代需重点关注的新方法、挑战与未来方向。我们提出了一种系统化的多层分类体系，对现有FedFM方法在模型训练、聚合、可信度及激励机制方面进行了分类。文中深入讨论了关键挑战，包括如何使FL应对高计算复杂度、隐私考量、贡献评估及通信效率等问题。此外，本文还探究了通过FL训练/微调FM过程中固有的通信、可扩展性与安全性方面的复杂挑战，并揭示了量子计算在革新模型训练、推理、优化及数据加密流程方面的潜力。本综述强调了推进FedFM创新研究的重要性，尤其需要开发可信赖的解决方案。它可为有意投身这一跨学科且快速演进领域的研究人员与实践者提供基础性指导。