Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models

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 …

availability can challenges computational cs.cr cs.lg data decentralized fairness federated federated learning foundation foundation models integration machine machine learning performance privacy resources robustness scalability