# When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions

**Authors:** Weiming Zhuang, Chen Chen, Lingjuan Lyu  
**arXiv:** [2306.15546](https://arxiv.org/abs/2306.15546)  
**Date:** 2023-06-27  
**Summary page:** https://www.summarizepaper.com/en/paper/2306.15546v1/

## Key points

- The paper explores the intersection of Foundation Model (FM) and Federated Learning (FL)
- FL expands availability of data for FMs by enabling computation sharing and distributing training process
- FM's size, pre-trained knowledge, and performance make it a robust starting point for FL
- Leveraging FM in FL facilitates faster convergence and achieves better performance even under non-iid data settings
- FM can be used to generate synthetic data, enriching data diversity, reducing overfitting issues, and preserving privacy
- The paper aims to deepen understanding of the synergistic relationship between FL and FM
- It discusses motivations behind combining these approaches as well as challenges faced by each individually and how they intersect
- Advancements resulting from this integration will drive development of privacy-preserving and scalable AI systems

## Summary

The paper titled "When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions" explores the intersection of the Foundation Model (FM) and Federated Learning (FL) and highlights the mutual benefits and unique opportunities they offer in AI research. FL expands the availability of data for FMs by enabling computation sharing and distributing the training process. This reduces the burden on FL participants and promotes collaborative FM development. It democratizes the process, fostering inclusivity and innovation in AI research. On the other hand, FM's enormous size, pre-trained knowledge, and exceptional performance make it a robust starting point for FL. By leveraging FM, FL facilitates faster convergence and achieves better performance even under non-iid (non-independent identically distributed) data settings. Additionally, FM can be used to generate synthetic data which enriches data diversity, reduces overfitting issues, and preserves privacy. This capability further enhances FL's effectiveness in training models on decentralized data sources while maintaining privacy constraints. The paper aims to deepen our understanding of the synergistic relationship between FL and FM by examining their interplay. It discusses motivations behind combining these approaches as well as challenges faced by each individually and how they intersect. By doing so it inspires future research directions that can enhance both fields simultaneously. The authors believe that advancements resulting from this integration will drive the development of privacy-preserving and scalable AI systems. They encourage researchers to explore these areas further to unlock new possibilities in AI research while addressing critical challenges faced by current approaches.