Federation Opacity and the Promise of Federated Learning in Healthcare.
Federated learning (FL) is a machine learning (ML) approach that allows multiple devices or institutions to collaboratively train an ML model without sharing their local data with a third-party. It has recently received significant attention as a promising way to overcome longstanding ethical obstac...
| Publicado en: | American Journal of Bioethics Vol. 26; no. 8; pp. 119 - 133 |
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| Autores principales: | , , , |
| Formato: | glossary tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Aug2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=195895367&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 195895367 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15265161 FKZ jtl: American Journal of Bioethics issn: 15265161 maglogo: N pubinfo: dt: Aug2026 vid: 26 iid: 8 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 195895367 192064068 195895367 195895367 10.1080/15265161.2026.2637093 195895367 ppf: 119 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Federation Opacity and the Promise of Federated Learning in Healthcare. aug: au: Hatherley, Joshua Søgaard, Anders Ballantyne, Angela Pauwels, Ruben affil: University of Copenhagen sug: subj: Federated Learning Ethical Issues Health Care Delivery Collaboration Ethics, Medical Machine Learning Algorithms Accountability Data Security Privacy and Confidentiality Philosophy, Medical Information Storage Information Retrieval Decision Making Rare Diseases Diagnosis Patient Safety ab: Federated learning (FL) is a machine learning (ML) approach that allows multiple devices or institutions to collaboratively train an ML model without sharing their local data with a third-party. It has recently received significant attention as a promising way to overcome longstanding ethical obstacles to training medical ML models with patient health data. This paper examines the promise of FL in healthcare from an ethical perspective. It argues that medical FL generates a new variety of opacity – federation opacity, wherein stakeholders cannot access, analyze, or curate the data on which a model has been trained – which (a) presents distinctive ethical challenges concerning institutional fairness and accountability in medical ML; and (b) makes FL models especially vulnerable to data poisoning attacks. It then identifies several key claims about the expected benefits of FL in healthcare and argues that they may be either exaggerated, misleading, or incomplete – often due to the problem of federation opacity. pubtype: Academic Journal doctype: glossary tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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