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Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

Sixing Yu, J. Pablo Muñoz, Ali Jannesari

TL;DR

This work introduces Federated Foundation Models (FFMs), a paradigm that merges Federated Learning with the lifecycle of Foundation Models to enable privacy-preserving, collaborative learning across distributed data sources. It proposes two-phased strategies for pre-training and fine-tuning, plus federated prompt tuning and lifelong learning, enabling personalization while mitigating data leakage. The paper outlines a suite of prospective tasks (FFM pre-training, FFM fine-tuning, FRAG, etc.) and enumerates extensive challenges (data heterogeneity, communication costs, security, edge-resource constraints) and future directions (edge hardware, collaborative compression, and PEFT). By enabling edge-based, decentralized optimization of large models, FFMs promise scalable, privacy-conscious AI that can adapt rapidly to user contexts and emerging data. The significance lies in providing a flexible framework that could accelerate FM development and FL algorithms in privacy-sensitive domains such as healthcare, finance, and IoT.

Abstract

Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training. However, optimizing FMs often requires access to sensitive data, raising privacy concerns and limiting their applicability in many domains. In this paper, we propose the Federated Foundation Models (FFMs) paradigm, which combines the benefits of FMs and Federated Learning (FL) to enable privacy-preserving and collaborative learning across multiple end-users. We discuss the potential benefits and challenges of integrating FL into the lifespan of FMs, covering pre-training, fine-tuning, and application. We further outline potential future research avenues in FFM, including FFM pre-training, FFM fine-tuning, and federated prompt tuning, which allow the development of more personalized and context-aware models while ensuring data privacy. Moreover, we explore the possibility of continual/lifelong learning in FFMs, as increased computational power at the edge may unlock the potential for optimizing FMs using newly generated private data close to the data source. The proposed FFM concepts offer a flexible and scalable framework for training large language models in a privacy-preserving manner, setting the stage for subsequent advancements in both FM training and federated learning.

Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models

TL;DR

This work introduces Federated Foundation Models (FFMs), a paradigm that merges Federated Learning with the lifecycle of Foundation Models to enable privacy-preserving, collaborative learning across distributed data sources. It proposes two-phased strategies for pre-training and fine-tuning, plus federated prompt tuning and lifelong learning, enabling personalization while mitigating data leakage. The paper outlines a suite of prospective tasks (FFM pre-training, FFM fine-tuning, FRAG, etc.) and enumerates extensive challenges (data heterogeneity, communication costs, security, edge-resource constraints) and future directions (edge hardware, collaborative compression, and PEFT). By enabling edge-based, decentralized optimization of large models, FFMs promise scalable, privacy-conscious AI that can adapt rapidly to user contexts and emerging data. The significance lies in providing a flexible framework that could accelerate FM development and FL algorithms in privacy-sensitive domains such as healthcare, finance, and IoT.

Abstract

Foundation Models (FMs), such as LLaMA, BERT, GPT, ViT, and CLIP, have demonstrated remarkable success in a wide range of applications, driven by their ability to leverage vast amounts of data for pre-training. However, optimizing FMs often requires access to sensitive data, raising privacy concerns and limiting their applicability in many domains. In this paper, we propose the Federated Foundation Models (FFMs) paradigm, which combines the benefits of FMs and Federated Learning (FL) to enable privacy-preserving and collaborative learning across multiple end-users. We discuss the potential benefits and challenges of integrating FL into the lifespan of FMs, covering pre-training, fine-tuning, and application. We further outline potential future research avenues in FFM, including FFM pre-training, FFM fine-tuning, and federated prompt tuning, which allow the development of more personalized and context-aware models while ensuring data privacy. Moreover, we explore the possibility of continual/lifelong learning in FFMs, as increased computational power at the edge may unlock the potential for optimizing FMs using newly generated private data close to the data source. The proposed FFM concepts offer a flexible and scalable framework for training large language models in a privacy-preserving manner, setting the stage for subsequent advancements in both FM training and federated learning.
Paper Structure (16 sections, 2 figures, 1 table, 2 algorithms)

This paper contains 16 sections, 2 figures, 1 table, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Federated Learning
  4. Foundation Models
  5. Motivation for Federated Foundation Models
  6. Federated Foundation Model: Prospective and Future Research
  7. Pre-training of Federated Foundation Models
  8. Federated Foundation Model Fine-tuning
  9. Federated Prompt Tuning
  10. Federated Continual (Lifelong) Learning
  11. Federated Retrieval Augmented Generation
  12. Challenges
  13. Other Future Research Directions
  14. Conclusion and discussion
  15. Acknowledgment
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Federated Foundation Model: Integrating federated learning into the lifespan of foundation models, facilitating privacy-preserving, scalable, lifelong learning, robustness, and decentralized FMs.
  • Figure 2: Federated Foundation Model tasks: The FFM centralized optimization process aggregates local models and updates them using public data. Private clients download up-to-date global model parameters from the server, optimize the FM locally on their tasks, and send the optimized model back to the server.