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[2406.12844] Synergizing Foundation Models and Federated Learning: A Survey
Summary
This survey explores the integration of Foundation Models (FMs) and Federated Learning (FL), termed Federated Foundation Models (FedFM), highlighting their potential in privacy-preserving AI applications.
Why It Matters
The synergy between Foundation Models and Federated Learning represents a significant advancement in AI, enabling collaborative learning while maintaining data privacy. This is particularly relevant in sectors where data sensitivity is paramount, such as healthcare and finance. Understanding this integration can guide researchers and practitioners in leveraging these technologies for innovative solutions.
Key Takeaways
- Federated Foundation Models (FedFM) enhance collaborative model adaptation while preserving data privacy.
- The paper provides a multi-tiered taxonomy based on efficiency, adaptability, and trustworthiness.
- A thorough review of existing libraries and benchmarks aids practical implementation.
- Real-world applications of FedFM span multiple domains, showcasing its versatility.
- Future research directions are outlined to encourage advancements in the field.
Computer Science > Machine Learning arXiv:2406.12844 (cs) [Submitted on 18 Jun 2024 (v1), last revised 16 Feb 2026 (this version, v2)] Title:Synergizing Foundation Models and Federated Learning: A Survey Authors:Shenghui Li, Fanghua Ye, Meng Fang, Jiaxu Zhao, Yun-Hin Chan, Edith C. H. Ngai, Thiemo Voigt View a PDF of the paper titled Synergizing Foundation Models and Federated Learning: A Survey, by Shenghui Li and 6 other authors View PDF HTML (experimental) 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 applica...
