The recent development of Foundation Models (FMs), represented by large
language models, vision transformers, and multimodal models, has been making a
significant impact on both academia and industry. Compared with small-scale
models, FMs have a much stronger demand for high-volume data during the
pre-training phase. Although general FMs can be pre-trained on data collected
from open sources such as the Internet, domain-specific FMs need proprietary
data, posing a practical challenge regarding the amount of data available due
to privacy concerns. Federated Learning (FL) is a collaborative learning
paradigm that breaks the barrier of data availability from different
participants. Therefore, it provides a promising solution to customize and
adapt FMs to a wide range of domain-specific tasks using distributed datasets
whilst preserving privacy. This survey paper discusses the potentials and
challenges of synergizing FL and FMs and summarizes core techniques, future
directions, and applications. A periodically updated paper collection on FM-FL
is available at this https URL

基于联邦学习与基础模型的潜力和挑战以及核心技术、未来方向和应用的综述。