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.

翻译：联邦学习作为分布式机器学习的重大突破，面临着数据可用性有限和计算资源差异等显著挑战，这些因素可能制约模型性能与可扩展性。将基础模型集成至联邦学习中，为解决上述问题提供了引人注目的方案——通过预训练与数据增强，有望提升数据丰富度并降低计算需求。然而，这种集成在鲁棒性、隐私性与公平性维度引入了现有研究尚未充分解决的新问题。我们通过系统评估联邦学习-基础模型集成在上述维度的影响，对该领域进行了初步探究：分析了其中涉及的权衡取舍，揭示了该集成引入的威胁与问题，并提出了应对这些挑战的标准与策略体系。此外，我们识别了推动该领域发展的潜在研究方向，为构建可靠、安全且公平的联邦学习系统奠定了未来发展的基础。