Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
This work addresses critical reliability and equity problems for developers and users of federated learning systems, but it is incremental as it builds on existing FL and FM research.
The paper investigates the integration of Foundation Models into Federated Learning to address data and computational challenges, but finds it introduces new issues in robustness, privacy, and fairness, proposing criteria and strategies for mitigation.
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.