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

TL;DR

FM and FL intersect to address data and compute barriers in Foundation Model development while enabling FM-driven enhancements for Federated Learning. The paper provides a dual-perspective, structured analysis of motivations, challenges, and opportunities for integrating FM with FL, plus future directions and system considerations. It offers a taxonomy of data, privacy, security, incentive, and infrastructure challenges, alongside avenues such as synthetic data, prompt-based sharing, personalization, and knowledge distillation. The work aims to guide researchers and practitioners toward privacy-preserving, scalable FM deployment via FL and to empower FL with FM-derived capabilities in real-world, heterogeneous settings.

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.

Figures (7)

• Figure 1: Illustration of foundation models.
• Figure 2: Illustration of federated learning.
• Figure 3: Organization of the paper.
• Figure 4: Resource sharing empowered by FL for FM.
• Figure 5: Illustration of possible approaches for empowering Foundation Model (FM) with Federated Learning (FL): (a) FL can be utilized to train FM from scratch or fine-tune FM using private client data. (b) Another approach is to utilize FL to train FM from scratch with model parallelisms (splitting the model into parts to train in parallel) using private client data. (c) FL can also be employed to allow clients to conduct parameter-efficient fine-tuning for their local FMs by training an additional adapter or using prompt tuning methods. These approaches illustrate the potential of empowering FM with FL.