  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.
