Position Paper: Assessing Robustness, Privacy, and Fairness in Federated
  Learning Integrated with Foundation Models
Xi Li Jiaqi Wang
Federated Learning (FL), while a breakthrough in decentralized machine
learning, contends with significant challenges such as limited data
availability and the variability of computational resources, which can stifle
the performance and scalability of the models. The integration of Foundation
Models (FMs) into FL presents a compelling solution to these issues, with the
potential to enhance data richness and reduce computational demands through
pre-training and data augmentation. However, this incorporation introduces
novel issues in terms of robustness, privacy, and fairness, which have not been
sufficiently addressed in the existing research. We make a preliminary
investigation into this field by systematically evaluating the implications of
FM-FL integration across these dimensions. We analyze the trade-offs involved,
uncover the threats and issues introduced by this integration, and propose a
set of criteria and strategies for navigating these challenges. Furthermore, we
identify potential research directions for advancing this field, laying a
foundation for future development in creating reliable, secure, and equitable
FL systems.