[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85923-en":3,"doc-seo-85923-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85923,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Toward Production-Ready Federated Learning in Healthcare","Healthcare organizations face strict constraints on centralizing patient data because medical records are sensitive, regulated, and institutionally controlled. Federated learning enables shared model training while keeping raw data local, yet it is not automatically production-ready or private: updates may still leak information and deployment requires robust handling of monitoring, rollback, debugging, and governance. The work examines how MLOps and Federated Learning Operations (FLOps) improve scalability, reliability, and trust through orchestration, privacy trade-offs, and post-deployment governance practices.","Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps  \nSakshi Gorkhali  \nJarvis College of Computing and Digital Media DePaul University Chicago Illinois United States [sgorkhal@depaul.edu](sgorkhal@depaul.edu)  \nJonesh Shrestha  \nJarvis College of Computing and Digital Media DePaul University Chicago Illinois United States [jshresth@depaul.edu](jshresth@depaul.edu)  \nABSTRACT  \nHealthcare 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.  \nCCS CONCEPTS  \n• Computing methodologies → Machine learning • Security and privacy → Privacy-preserving protocols • Software and its engineering → Software organization and properties → Software system structures → Software architectures • Applied computing → Health care information systems  \nKEYWORDS  \nFederated learning, healthcare AI, MLOps, FLOps, privacypreserving machine learning, secure aggregation, differential privacy, model governance, drift monitoring  \nACM Reference format:  \nSakshi Gorkhali and Jonesh Shrestha 2026. Toward Production-Ready Federated Learning in Healthcare: Privacy, Orchestration, and Governance in MLOps. Preprint submitted to arXiv. DePaul University, Chicago, IL, USA. 5 pages.  \n1 Introduction  \nHealthcare AI depends on diverse data, but hospitals and research centers often cannot pool raw patient records into a single repository. HIPAA requires administrative, physical, and technical safeguards for electronic protected health information in the United States [1], while the GDPR treats health data as a special category subject to stricter protections in the European Union [2] . These constraints make centralized model training difficult in many healthcare settings.  \nFederated learning (FL) addresses this problem by moving training to the data instead of moving data to a central server. In atypical FL system, each hospital trains the model on its own local data and sends only model updates, such as weights or gradients, to a coordinating server. The server combines those updates into anew global model and sends the improved model back to the hospitals for the next round of training [3] . This allows institutions to collaborate without directly sharing raw patient records.  \nThis approach is especially useful in healthcare, where model performance can improve when training reflects data from multiple institutions, patient populations, and clinical settings. Rieke et al. describe FL as a promising direction for digital health because it can help overcome data silos while respecting institutional boundaries [4] . However, FL does not remove all privacy and production risks. Model gradients or parameter updates may still leak information about local","cbCaisV4ljZOPPQy","https://ap.wps.com/l/cbCaisV4ljZOPPQy","pdf",407893,4,1,5,"English","en",105,"# 1 Introduction\n# 2 Literature Review","[{\"question\":\"Why can’t healthcare organizations typically centralize patient data for model training?\",\"answer\":\"Medical records are sensitive and controlled by institutions, and regulations such as HIPAA and GDPR impose strict safeguards. These constraints make centralized training difficult across many healthcare settings.\"},{\"question\":\"How does federated learning reduce data centralization in healthcare?\",\"answer\":\"Each hospital trains locally and sends model updates such as weights or gradients to a coordinating server. The server aggregates updates to produce a new global model, which is returned for the next training round.\"},{\"question\":\"What production and governance gaps remain even when using federated learning?\",\"answer\":\"Model updates can still leak information without additional protections. A production system also needs version control, audit logging, drift monitoring, heterogeneity and failed-participant handling, rollback support, and clear governance to ensure long-term reliability.\"}]",1784207189,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"toward-production-ready-federated-learning-in-healthcare","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/toward-production-ready-federated-learning-in-healthcare/85923/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why can’t healthcare organizations typically centralize patient data for model training?","Question",{"text":75,"@type":76},"Medical records are sensitive and controlled by institutions, and regulations such as HIPAA and GDPR impose strict safeguards. These constraints make centralized training difficult across many healthcare settings.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does federated learning reduce data centralization in healthcare?",{"text":80,"@type":76},"Each hospital trains locally and sends model updates such as weights or gradients to a coordinating server. The server aggregates updates to produce a new global model, which is returned for the next training round.",{"name":82,"@type":73,"acceptedAnswer":83},"What production and governance gaps remain even when using federated learning?",{"text":84,"@type":76},"Model updates can still leak information without additional protections. A production system also needs version control, audit logging, drift monitoring, heterogeneity and failed-participant handling, rollback support, and clear governance to ensure long-term reliability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,109,114,119,122,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":22,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":22,"slug":137},19,"General","general"]