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

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AI Summary

The integration of federated learning (FL) with foundation models (FMs) introduces intertwined challenges in robustness, privacy, and fairness—yet existing work lacks a systematic characterization of their intrinsic interdependencies and trade-off mechanisms. Method: This project formally defines the “robustness-privacy-fairness” triadic synergy problem in FM-FL integration, establishes a cross-dimensional security assessment framework, and develops (i) a multi-dimensional security evaluation methodology, (ii) a joint privacy-fairness metric, and (iii) an FM adaptability analysis model. Contribution/Results: We identify 12 actionable evaluation criteria, categorize 5 governance strategies, and delineate 6 critical research directions. The work provides foundational theoretical insights and practical guidance for developing trustworthy federated intelligent systems, bridging gaps between FM capabilities and FL security requirements.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

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.

Problem

• Evaluating robustness, privacy, and fairness in federated learning with foundation models
• Addressing novel threats and trade-offs from integrating foundation models into federated learning
• Developing strategies for reliable, secure, and equitable federated learning systems

Innovation

• Integrates Foundation Models into Federated Learning
• Systematically evaluates robustness, privacy, and fairness issues
• Proposes criteria and strategies to address challenges