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.

综合调查了联邦基础模型（FedFM）领域，阐明了其与联邦学习（FL）的协同关系，并探索了FL研究领域在基础模型时代需要关注的新方法、挑战和未来方向。提出了一种系统的多层次分类法，对现有的FedFM方法进行了分类。全面讨论了关键挑战，包括如何使FL处理高复杂性的计算需求、隐私考虑、贡献评估和通信效率。这项调查强调了在FedFM领域进行进一步研究以推动创新的重要性，强调了开发可信解决方案的需求。作为研究人员和从业者参与这个跨学科和快速发展领域的基础指南。

基于基础模型的联邦学习的进展与开放挑战