使跨大陆联邦学习可复现：FLIP多应用研究
Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study
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中文总结 AI 辅助
针对医疗联邦学习治理开销大、难以复现的问题，提出开源平台FLIP，通过可组合服务实现跨大陆可复现的联邦训练与评估，并在英泰两节点上验证了其提升协作、可复现性与治理的效果。
中文摘要 AI 辅助
医疗保健中的联邦学习（FL）仍然具有挑战性，因为每次合作重建治理保障的开销使大多数项目止步于概念验证阶段。在此，我们提出FLIP（联邦学习互操作平台），一个开源的多应用平台，使FL训练和评估可复现。FLIP将常见FL工作流实现为一组可组合服务：针对各站点结构化数据库的队列查询、从机构PACS按需检索DICOM、各站点项目审批以及可复用的FL作业类型。为演示FLIP，我们在英国（UK）和泰国两个客户端节点上的合成胸部X光队列上运行了两个不同的用例：联邦微调和联邦评估。在FLIP中，每个机构独立批准其参与每个项目，并在本地IT安全流程下运行自己的节点。本研究提出的是操作性而非算法性的主张。它不比较联邦训练与集中式训练；对于该问题，我们请读者参考现有的系统综述和荟萃分析。核心结果是证明此类平台能够实现国际FL协作，并提高可复现性、可审计性和站点特定治理。我们还提供了现有平台的全面比较，以帮助研究人员和操作者为其用例选择合适的平台。
英文摘要
Federated learning (FL) in healthcare remains challenging, as the overhead of rebuilding governance guarantees for every collaboration stops most projects at the proof-of-concept stage. Here we present FLIP (Federated Learning Interoperability Platform), an open-source, multi-application platform that makes FL training and evaluation repeatable. FLIP implements common FL workflows as a set of composable services: cohort queries against per-site structured databases, on-demand DICOM retrieval from institutional PACS, per-site project approval, and reusable FL job types. To demonstrate FLIP, we ran two distinct use cases, federated fine-tuning and federated evaluation, on synthetic chest X-ray cohorts across two client nodes based in the United Kingdom (UK) and Thailand. In FLIP, each institution independently approves its participation in each project and operates its own node under local IT security processes. This study makes an operational rather than an algorithmic claim. It does not compare federated with centralised training; for that question, we refer the reader to existing systematic reviews and meta-analyses. The central result is evidence that such platforms enable international FL collaboration and improve repeatability, auditability, and site-specific governance. We also present a comprehensive comparison of existing platforms to help researchers and operators choose the right platform for their use case.
发表机构
- Bangkok Dusit Medical Services（曼谷杜斯特医疗服务公司）
机构由 AI 辅助整理，请以论文原文为准。
