The intersection of the Foundation Model (FM) and Federated Learning (FL) provides mutual benefits, presents a unique opportunity to unlock new possibilities in AI research, and address critical challenges in AI and real-world applications. FL expands the availability of data for FMs and enables computation sharing, distributing the training process and reducing the burden on FL participants. It promotes collaborative FM development, democratizing the process and fostering inclusivity and innovation. On the other hand, FM, with its enormous size, pre-trained knowledge, and exceptional performance, serves as a robust starting point for FL, facilitating faster convergence and better performance under non-iid data. Additionally, leveraging FM to generate synthetic data enriches data diversity, reduces overfitting, and preserves privacy. By examining the interplay between FL and FM, this paper aims to deepen the understanding of their synergistic relationship, highlighting the motivations, challenges, and future directions. Through an exploration of the challenges faced by FL and FM individually and their interconnections, we aim to inspire future research directions that can further enhance both fields, driving advancements and propelling the development of privacy-preserving and scalable AI systems.

翻译：基础模型（FM）与联邦学习（FL）的交叉融合具有双向增益，既为AI研究开辟了新可能，也为解决AI及实际应用中的关键挑战提供了独特机遇。联邦学习拓展了基础模型的数据可用性，通过计算共享机制分散训练过程，减轻了联邦学习参与方的计算负担；同时促进协作式基础模型开发，推动过程民主化，培育包容性与创新活力。另一方面，基础模型凭借其庞大规模、预训练知识与卓越性能，为联邦学习提供稳健起点，助力非独立同分布数据场景下的快速收敛与性能提升。此外，利用基础模型生成合成数据可增强数据多样性、降低过拟合风险并保护隐私。通过剖析联邦学习与基础模型的相互作用，本文旨在深化对二者协同关系的理解，系统阐述其动机、挑战与未来方向。通过分别探索联邦学习与基础模型面临的挑战及其内在关联，我们期望激发可同时推动两个领域发展的未来研究方向，驱动技术进步，促进隐私保护与可扩展AI系统的构建。