基于美国科学云的联邦学习：使用APPFL框架

中文摘要 AI 辅助

美国科学云（AmSC）是在美国能源部（DOE）创世纪任务框架下建立的，旨在将DOE的高性能计算系统、实验设施和数据资源整合为一个统一、协调的AI驱动发现平台。AmSC的早期服务聚焦于经过整理的人工制品，例如对托管模型的门控推理访问、实验跟踪以及跨计算设施的功能执行。然而，这些服务缺少一种在组织边界间训练模型的手段——由于政策、隐私或规模限制，数据无法集中化，这正是联邦学习（FL）的典型应用场景，也是科学AI的一个新兴类别。本文展示，可通过将高级隐私保护联邦学习（APPFL）框架的编排逻辑作为可扩展云服务部署在AmSC已提供的基础原语之上，填补这一空白：这些原语包括支持安全可靠联邦成员资格的项目范围认证、驱动各站点分布式训练的功能执行、记录轮次级性能的实验跟踪，以及可用于将联邦训练后的模型分发至授权参与者的模型托管与推理基础设施。我们认为，将联邦计算作为AmSC的一项重要服务提供，将解锁受隐私约束的科学合作，实现模型构建中的公私伙伴关系，同时锻炼并增强平台自身的联邦基础设施。

英文摘要

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.

发表机构

• Argonne National Laboratory（阿贡国家实验室）
• University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
• Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）

机构由 AI 辅助整理，请以论文原文为准。