Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and computer vision tasks. Grounding FMs by adapting them to domain-specific tasks or augmenting them with domain-specific knowledge enables us to exploit the full potential of FMs. However, grounding FMs faces several challenges, stemming primarily from constrained computing resources, data privacy, model heterogeneity, and model ownership. Federated Transfer Learning (FTL), the combination of federated learning and transfer learning, provides promising solutions to address these challenges. In recent years, the need for grounding FMs leveraging FTL, coined FTL-FM, has arisen strongly in both academia and industry. Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy. We also establish correspondences between FTL-FM and conventional phases of adapting FM so that FM practitioners can align their research works with FTL-FM. In addition, we overview advanced efficiency-improving and privacy-preserving techniques because efficiency and privacy are critical concerns in FTL-FM. Last, we discuss opportunities and future research directions of FTL-FM.

翻译：基础模型（如GPT-4）凭借其海量知识与强大的涌现能力，已在各类自然语言处理和计算机视觉任务中取得显著成功。通过将基础模型适配到特定领域任务或增强其领域知识，可实现模型潜能的完全释放。然而，这种"接地"过程面临计算资源受限、数据隐私、模型异构性及模型所有权等多重挑战。联邦迁移学习——联邦学习与迁移学习的融合——为应对这些挑战提供了可行方案。近年来，学术界与工业界对利用联邦迁移学习实现基础模型接地（简称FTL-FM）的需求日益迫切。基于FTL-FM研究的迅猛发展及其对工业应用的潜在影响，我们提出一个FTL-FM框架：该框架形式化描述了联邦学习场景下的基础模型接地问题，构建了基于FTL-FM的细粒度分类体系以归类现有前沿工作，并基于该分类法对FTL-FM研究进行了全面综述。同时，我们建立了FTL-FM与基础模型传统适配阶段之间的对应关系，使基础模型实践者能将其研究与FTL-FM对齐。此外，鉴于效率与隐私是FTL-FM的关键考量，我们综述了先进的效率优化与隐私保护技术。最后，探讨了FTL-FM的发展机遇与未来研究方向。