迈向医疗保健领域可投入生产的联邦学习：MLOps 中的隐私、编排与治理
Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps
浏览论文内容
中文总结 AI 辅助
研究医疗保健领域联邦学习，探讨 MLOps 实践和 FLOps 理念，回答容器化编排对联邦部署的支持、隐私机制的影响及部署后重要实践等问题，提出需集成 MLOps 架构让联邦医疗保健机器学习系统更具性能。
中文摘要 AI 辅助
医疗保健组织通常无法自由集中患者数据，因为病历敏感、受监管且由机构控制。联邦学习提供了一种实用的替代方案，允许医院和诊所训练共享模型，同时将原始数据保留在本地。然而，联邦学习并非默认即可投入生产或具备隐私性。模型更新仍可能泄露信息，分散式训练在部署、监控、回滚、调试和治理方面带来操作挑战。本文探讨 MLOps 实践和新兴的联邦学习操作（FLOps）理念如何使联邦医疗保健机器学习系统具有可扩展性、可靠性和可信度。它回答了三个研究问题：容器化和编排如何支持联邦部署，隐私保护机制如何影响隐私、效用、可扩展性和操作复杂性之间的权衡，以及哪些部署后实践对长期治理最为重要。核心观点是，联邦医疗保健机器学习需要的不仅仅是隐私保护算法。它需要一个集成的 MLOps 架构，结合可重复部署、安全编排、模型版本控制、审计日志记录、漂移监控、异构性管理和明确的治理。
英文摘要
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.
发表机构
- Jarvis College of Computing and Digital Media（贾维斯计算与数字媒体学院）
- DePaul University（德保罗大学）
机构由 AI 辅助整理，请以论文原文为准。
