Foundation Models (FMs), such as 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 certain domains. In this paper, we introduce the concept of Federated Foundation Models (FFMs), a novel approach that combines the benefits of FMs and Federated Learning (FL) to enable privacy-preserving and collaborative learning across multiple institutions. We discuss the potential benefits and challenges of integrating FL into the lifespan of FMs, covering pre-training, fine-tuning, and application. We further provide formal definitions of FFM tasks, including FFM pre-training, FFM fine-tuning, and federated prompt engineering, allowing for more personalized and context-aware models while maintaining data privacy. Moreover, we explore the possibility of continual/lifelong learning in FFMs, as increased computational power at the edge unlocks the potential for optimizing FMs using newly generated private data at edges. We present experiments and evaluations comparing the performance of FFMs to traditional FMs on various downstream tasks, demonstrating the effectiveness of our approach in preserving privacy, reducing overfitting, and improving model generalizability. The proposed Federated Foundation Models offer a flexible and scalable framework for training large language models in a privacy-preserving manner, paving the way for future advancements in both FM pre-training and federated learning.

本文介绍了一种结合了联邦学习和基础模型的隐私保存方法，即联邦基础模型，其中涉及该方法的不同方面和任务，以及与传统基础模型的比较和实验。

联邦基础模型：大型模型的隐私保护和协同学习