Secure Federated Learning Across Heterogeneous Cloud and
  High-Performance Computing Resources -- A Case Study on Federated Fine-tuning
  of LLaMA 2
Zilinghan Li Shilan He Pranshu Chaturvedi Volodymyr Kindratenko Eliu A Huerta Kibaek Kim Ravi Madduri
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.