  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.
