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

TL;DR

This paper explores the synergy between Foundation Models and Federated Learning, highlighting how their integration can address challenges and unlock new capabilities in collaborative AI development.

Contribution

It provides a comprehensive overview of motivations, challenges, and future directions for combining FM and FL, offering insights to guide future research.

Findings

FL expands data access for FM development

FM enhances FL with synthetic data and multi-modal capabilities

Integration addresses data privacy and collaboration challenges

Abstract

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.