Synergizing Foundation Models and Federated Learning: A Survey

TL;DR

This survey reviews the emerging integration of Foundation Models with Federated Learning, highlighting their taxonomy, practical tools, applications, and future research directions in privacy-preserving AI.

Contribution

It provides a comprehensive taxonomy, reviews existing tools and benchmarks, and discusses real-world applications of Federated Foundation Models.

Findings

Taxonomy based on efficiency, adaptability, trustworthiness

Review of libraries and benchmarks for FedFM

Discussion of diverse real-world applications

Abstract

Over the past few years, the landscape of Artificial Intelligence (AI) has been reshaped by the emergence of Foundation Models (FMs). Pre-trained on massive datasets, these models exhibit exceptional performance across diverse downstream tasks through adaptation techniques like fine-tuning and prompt learning. More recently, the synergy of FMs and Federated Learning (FL) has emerged as a promising paradigm, often termed Federated Foundation Models (FedFM), allowing for collaborative model adaptation while preserving data privacy. This survey paper provides a systematic review of the current state of the art in FedFM, offering insights and guidance into the evolving landscape. Specifically, we present a comprehensive multi-tiered taxonomy based on three major dimensions, namely efficiency, adaptability, and trustworthiness. To facilitate practical implementation and experimental research, we undertake a thorough review of existing libraries and benchmarks. Furthermore, we discuss the diverse real-world applications of this paradigm across multiple domains. Finally, we outline promising research directions to foster future advancements in FedFM. Overall, this survey serves as a resource for researchers and practitioners, offering a thorough understanding of FedFM's role in revolutionizing privacy-preserving AI and pointing toward future innovations in this promising area. A periodically updated paper collection on FM-FL is available at https://github.com/lishenghui/awesome-fm-fl.