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

arXiv:2305.11414v3 Announce Type: replace-cross
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 …

access applications arxiv bert cs.ai cs.cr cs.lg data federated foundation foundation models gpt large llama privacy privacy concerns sensitive sensitive data training vast