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，已在自然语言处理和计算机视觉等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用领域特定知识对其进行增强，能够充分释放基础模型的潜力。然而，受限于计算资源约束、数据隐私、模型异构性及模型所有权等问题，基础模型的落地仍面临诸多挑战。联邦迁移学习作为联邦学习与迁移学习的融合范式，为解决这些挑战提供了有效方案。近年来，学术界与工业界对基于联邦迁移学习落地基础模型（简称FTL-FM）的需求日益迫切。鉴于FTL-FM研究的蓬勃发展和其对工业应用的潜在影响，本文提出FTL-FM框架：该框架在联邦学习场景中系统阐述了基础模型落地问题，基于该框架构建了细粒度分类体系以归类前沿FTL-FM研究，并依据所提分类体系对现有FTL-FM工作进行全面综述。我们还将FTL-FM与基础模型适配的传统阶段建立映射关系，便于基础模型研究者将自身工作与FTL-FM对齐。此外，考虑到效率与隐私是FTL-FM的关键关切，本文综述了先进的效率提升与隐私保护技术。最后，我们探讨了FTL-FM的发展机遇与未来研究方向。