Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps
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 sy...
| Authors | Sakshi Gorkhali, Jonesh Shrestha |
|---|---|
| Primary category | cs.SE |
| Published | 11 July 2026 |
| Last updated at source | 14 July 2026 |
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