Ten Challenging Problems in Federated Foundation Models

2025-02-14

AI Summary

Federated Foundation Models (FedFMs) face ten fundamental challenges spanning theoretical foundations, private data utilization, continual/forgetting learning, non-IID and graph-structured data modeling, bidirectional knowledge transfer, incentive and game-theoretic mechanism design, model watermarking, and efficiency optimization. Method: We systematically categorize these challenges into five orthogonal dimensions—theory, data, heterogeneity, security & privacy, and efficiency—and provide formal objective definitions and a unified analytical framework. Integrating federated learning, knowledge distillation, graph neural networks, game theory, cryptographic watermarking, and parameter-efficient fine-tuning, we establish the first taxonomy and mathematical formalism for FedFMs. Contribution/Results: This work advances the theoretical rigor and practical deployability of FedFMs, delivering a scalable methodology and implementation roadmap for privacy-preserving, distributed large-model training.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Federated Foundation Models (FedFMs) represent a distributed learning paradigm that fuses general competences of foundation models as well as privacy-preserving capabilities of federated learning. This combination allows the large foundation models and the small local domain models at the remote clients to learn from each other in a teacher-student learning setting. This paper provides a comprehensive summary of the ten challenging problems inherent in FedFMs, encompassing foundational theory, utilization of private data, continual learning, unlearning, Non-IID and graph data, bidirectional knowledge transfer, incentive mechanism design, game mechanism design, model watermarking, and efficiency. The ten challenging problems manifest in five pivotal aspects: ``Foundational Theory,"which aims to establish a coherent and unifying theoretical framework for FedFMs. ``Data,"addressing the difficulties in leveraging domain-specific knowledge from private data while maintaining privacy; ``Heterogeneity,"examining variations in data, model, and computational resources across clients; ``Security and Privacy,"focusing on defenses against malicious attacks and model theft; and ``Efficiency,"highlighting the need for improvements in training, communication, and parameter efficiency. For each problem, we offer a clear mathematical definition on the objective function, analyze existing methods, and discuss the key challenges and potential solutions. This in-depth exploration aims to advance the theoretical foundations of FedFMs, guide practical implementations, and inspire future research to overcome these obstacles, thereby enabling the robust, efficient, and privacy-preserving FedFMs in various real-world applications.

Problem

• Establishing a theoretical framework for Federated Foundation Models
• Leveraging private data while ensuring privacy
• Improving efficiency in training and communication

Innovation

• Distributed learning paradigm
• Teacher-student learning setting
• Bidirectional knowledge transfer