Federated learning enables multiple data owners to collaboratively train
robust machine learning models without transferring large or sensitive local
datasets by only sharing the parameters of the locally trained models. In this
paper, we elaborate on the design of our Advanced Privacy-Preserving Federated
Learning (APPFL) framework, which streamlines end-to-end secure and reliable
federated learning experiments across cloud computing facilities and
high-performance computing resources by leveraging Globus Compute, a
distributed function as a service platform, and Amazon Web Services. We further
demonstrate the use case of APPFL in fine-tuning a LLaMA 2 7B model using
several cloud resources and supercomputers.

通过仅共享本地训练模型的参数，联邦学习可以使多个数据所有者合作训练强大的机器学习模型，而无需传输大量或敏感的本地数据。本文详细介绍了我们的高级隐私保护联邦学习（APPFL）框架的设计，通过利用 Globus Compute，一个分布式函数即服务平台和亚马逊云服务，在云计算设施和高性能计算资源之间实现端到端的安全可靠的联邦学习实验流程。我们进一步演示了在几个云资源和超级计算机上使用 APPFL 对 LLaMA 2 7B 模型进行微调的用例。