Ten Challenging Problems in Federated Foundation Models

TL;DR

FedFMs fuse foundation models with federated learning to enable cross-domain knowledge exchange while preserving privacy. The paper introduces ten challenging problems across foundational theory, data handling, heterogeneity, security/privacy, and efficiency, each with formal objective formulations, reviews of existing methods, and potential solutions. A unified multi-objective framework is proposed to balance utility, privacy, efficiency, watermarking, contribution evaluation, and other losses, highlighting the inherent trade-offs and no-free-lunch constraints. The work advances the theoretical underpinnings of FedFMs and provides a structured roadmap for deploying robust, privacy-preserving, and scalable FedFMs in real-world domains such as healthcare, finance, and IoT.

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.

Figures (11)

• Figure 1: An illustration of Federated Foundation Models (FedFMs). On one hand, Domain Models (DMs) can augment the domain-specific knowledge of Foundation Models (FMs) through FedFMs. On the other hand, FMs can assist in enhancing the generalization capabilities of DMs on the edges in a distributed setting.
• Figure 2: Pareto Curve of Privacy-Utility Trade-off
• Figure 3: FedFMs utilize locally stored private data from organizations (toB) or client devices (toC) to train models while ensuring privacy, enabling applications like disease diagnosis and health analysis 6-data-fedcampus.
• Figure 4: An illustration of Continual Learning with FedFMs.
• Figure 5: Machine Unlearning in FedFMs.