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This paper addresses the limitations of the American Science Cloud (AmSC) in facilitating federated learning (FL) across organizational boundaries, which is crucial for scientific collaboration where data cannot be centralized. By implementing the Advanced Privacy-Preserving Federated Learning (APPFL) framework as a cloud service, the authors demonstrate how to leverage AmSC's existing infrastructure for secure and scalable distributed model training. The key result shows that this approach not only enhances privacy-constrained collaborations but also strengthens the federated infrastructure of AmSC itself.
Federated learning can now thrive in privacy-sensitive scientific collaborations, unlocking new avenues for public-private partnerships in AI model development.
The American Science Cloud (AmSC), established under the Genesis Mission of the U.S. Department of Energy (DOE), aims to integrate DOE high-performance computing systems, experimental facilities, and data resources into a single, coordinated, AI-driven discovery platform. AmSC's early services focus on curated artifacts, such as gated inference access to hosted models, experiment tracking, and function execution across computing facilities. However, what these services lack is a means to train a model across organizational boundaries where data cannot be centralized due to policy, privacy, or scale. This is, by definition, a use case for federated learning (FL) and a growing class of scientific AI. In this paper, we show that this gap can be bridged by deploying the orchestration logic of the Advanced Privacy-Preserving Federated Learning (APPFL) framework as a scalable cloud service on top of the primitives AmSC already provides: project-scoped authentication that supports secure and reliable federation membership, function execution that drives distributed training at each site, experiment tracking that records round-level performance, and finally, the model-hosting and inference infrastructure that can be leveraged to distribute the federated trained models to authorized participants. We argue that offering federated computing as an important AmSC service would unlock privacy-constrained scientific collaborations, enabling public-private partnerships in model building while exercising and enhancing the platform's own federated infrastructure.
