Abstract
The integration of Foundation Models (FMs) with Federated Learning (FL) presents a transformative paradigm in Artificial Intelligence (AI), offering enhanced capabilities while addressing concerns of privacy, data decentralization, and computational efficiency. This paper provides a comprehensive survey of the emerging field of Federated Foundation Models (FedFM), elucidating their synergistic relationship and exploring novel methodologies, challenges, and future directions that the FL research field needs to focus on in order to thrive in the age of foundation models. A systematic multi-tiered taxonomy is proposed, categorizing existing FedFM approaches for model training, aggregation, trustworthiness, and incentivization. Key challenges, including how to enable FL to deal with high complexity of computational demands, privacy considerations, contribution evaluation, and communication efficiency, are thoroughly discussed. Moreover, the paper explores the intricate challenges of communication, scalability and security inherent in training/fine-tuning FMs via FL, highlighting the potential of quantum computing to revolutionize the training, inference, optimization and data encryption processes. This survey underscores the importance of further research to propel innovation in FedFM, emphasizing the need for developing trustworthy solutions. It serves as a foundational guide for researchers and practitioners interested in contributing to this interdisciplinary and rapidly advancing field.
Abstract (translated)
将基础模型（FMs）与去中心化学习（FL）相结合，为人工智能（AI）领域提供了一种变革性的范式，同时解决了对隐私、数据去中心化和计算效率的担忧。本文对新兴领域Federated Foundation Models（FedFM）进行全面调查，阐述其协同关系，并探讨了FL研究领域需要关注的新方法、挑战和未来方向，以便在基础模型时代蓬勃发展。提出了一种多层分类体系，对现有的FedFM模型进行分类，包括模型训练、聚合、可信度和激励。详细讨论了如何通过FL处理计算需求的复杂性、隐私考虑、贡献评估和通信效率等关键挑战。此外，本文探讨了通过FL训练/微调FMs所面临的精细挑战，强调了量子计算在革新训练、推理、优化和数据加密过程中的潜在可能性。这项调查强调了对FedFM进一步研究以推动创新的必要性，需要开发可靠的解决方案。它为对此跨学科且快速发展的领域感兴趣的研究人员和实践者提供了一个基础指南。
URL
https://arxiv.org/abs/2404.15381
