构建可落地的医疗联邦学习系统，解决隐私、运维与治理难题
Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps
- 用容器化与编排实现医疗数据本地训练的可靠部署
- 隐私保护机制在隐私、效果、扩展性间存在权衡
- 需结合版本管理、审计日志等实现长期系统治理
医疗组织因患者数据敏感、受监管且由机构控制，难以自由集中数据。联邦学习通过让医院和诊所本地训练共享模型，保留原始数据隐私，提供可行替代方案。但联邦学习并非天然具备生产就绪性或隐私保障。模型更新仍可能泄露信息，去中心化训练带来部署、监控、回滚、调试和治理等运维挑战。本文探讨MLOps实践及新兴的联邦学习运维（FLOps）如何使医疗联邦机器学习系统具备可扩展性、可靠性与可信度。回答三个研究问题：容器化与编排如何支持联邦部署；隐私保护机制对隐私、效用、可扩展性与操作复杂性的权衡；哪些上线后实践对长期治理最为关键。核心观点是，医疗联邦学习不仅需要隐私算法，更需整合可复现部署、安全编排、模型版本管理、审计日志、漂移监控、异构性管理与明确治理的MLOps架构。
原文摘要 · Abstract (English)
Healthcare organizations often cannot freely centralize patient data because medical records are sensitive, regulated, and institutionally controlled. Federated learning offers a practical alternative by allowing hospitals and clinics to train a shared model while keeping raw data local. However, federated learning is not automatically production-ready or private by default. Model updates can still leak information, and decentralized training introduces operational challenges in deployment, monitoring, rollback, debugging, and governance. This paper examines how MLOps practices and the emerging idea of Federated Learning Operations (FLOps) can make federated healthcare machine learning systems scalable, reliable, and trustworthy. It answers three research questions: how containerization and orchestration support federated deployment, how privacy-preserving mechanisms affect trade-offs among privacy, utility, scalability, and operational complexity, and which post-deployment practices are most important for long-term governance. The central argument is that federated healthcare ML requires more than privacy-preserving algorithms. It needs an integrated MLOps architecture that combines reproducible deployment, secure orchestration, model versioning, audit logging, drift monitoring, heterogeneity management, and clear governance.
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