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）凭借其蕴含的广泛知识及强大的涌现能力，已在自然语言处理和计算机视觉等领域的各项任务中取得显著成功。通过适配领域特定任务或注入领域特定知识来落地基础模型，能够充分发挥其全部潜力。然而，基础模型落地面临若干挑战，主要源于有限的计算资源、数据隐私、模型异构性及模型所有权问题。联邦迁移学习作为联邦学习与迁移学习的融合，为解决上述挑战提供了极具前景的方案。近年来，学术界与工业界对基于联邦迁移学习实现基础模型落地（简称FTL-FM）的需求日益迫切。鉴于FTL-FM研究的蓬勃发展及其对工业应用的潜在影响，本文提出一个FTL-FM框架，该框架系统阐述了在联邦学习场景下基础模型落地的问题构建方法，基于FTL-FM框架构建了详尽的分类体系以对现有FTL-FM研究进行归类，并依据所提分类体系对相关工作进行全面综述。此外，我们建立了FTL-FM与基础模型适配传统阶段之间的对应关系，使基础模型从业者能够将其研究工作与FTL-FM有效对齐。考虑到效率与隐私是FTL-FM中的关键问题，本文进一步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。