When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions
It identifies motivations and challenges for integrating these technologies, potentially benefiting researchers and practitioners in AI and distributed systems, but it is incremental as it reviews existing concepts without presenting new experimental results.
This paper explores the intersection of Foundation Models (FMs) and Federated Learning (FL), highlighting how FL can address challenges in FM development by expanding data availability and enabling collaborative efforts, while FMs can enhance FL by providing pre-trained knowledge and generating synthetic data to improve performance.
The intersection of Foundation Model (FM) and Federated Learning (FL) presents a unique opportunity to unlock new possibilities for real-world applications. On the one hand, FL, as a collaborative learning paradigm, help address challenges in FM development by expanding data availability, enabling computation sharing, facilitating the collaborative development of FMs, tackling continuous data update, avoiding FM monopoly, response delay and FM service down. On the other hand, FM, equipped with pre-trained knowledge and exceptional performance, can serve as a robust starting point for FL. It can also generate synthetic data to enrich data diversity and enhance overall performance of FL. Meanwhile, FM unlocks new sharing paradigm and multi-task and multi-modality capabilities for FL. By examining the interplay between FL and FM, this paper presents the motivations, challenges, and future directions of empowering FL with FM and empowering FM with FL. We hope that this work provides a good foundation to inspire future research efforts to drive advancements in both fields.