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

TL;DR

This position paper explores the integration of Foundation Models into Federated Learning, highlighting challenges in robustness, privacy, and fairness, and proposing strategies to address these issues for more reliable and equitable decentralized AI systems.

Contribution

It provides a systematic preliminary evaluation of FM-FL integration, identifying key challenges and proposing criteria and strategies for improving robustness, privacy, and fairness.

Findings

Analysis of trade-offs in FM-FL integration

Identification of privacy and fairness threats

Proposed strategies for enhancing robustness

Abstract

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.