Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

arXiv:2405.06784v1 Announce Type: new
Abstract: This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such as ChatGPT, LLaMa, and CLIP, which are trained on vast datasets through methods including unsupervised pretraining, self-supervised learning, instructed fine-tuning, and reinforcement learning from human feedback, represent significant advancements in machine learning. These models, with their ability to generate coherent text and realistic images, are crucial for …

abstract artificial artificial intelligence arxiv biomedical challenges chatgpt clip cs.lg datasets federated learning foundation healthcare impact integration intelligence llama opportunities pretraining research survey through type unsupervised vast