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October 12, 2026The American Journal of BioethicsOpen Access

Federation Opacity Makes Medical Machine Learning’s Failures Harder to Detect and Correct

Joshua Hatherley Anders Søgaard Angela Ballantyne Ruben Pauwels

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Abstract

Our target article argued that federated learning (FL) generates a distinctive variety of opacity—federation opacity, in which no party can access, analyze, or curate the model’s full training data...

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Cite This Study

Hatherley et al. (2026) studied this question.

synapsesocial.com/papers/6ad06c8c0baecf8d117b8883 https://doi.org/10.1080/15265161.2026.2741283

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Federation Opacity and the Promise of Federated Learning in HealthcareOriginal Research · 2026
2Privacy Protection Optimization in Federated Learning2025
3Federated Learning in Healthcare: Balancing Data Privacy and Predictive Accuracy in Multi-Institutional Settings2023 · 2 citations
4Federated Learning Models for Privacy-Preserving Medical Image Analysis2025 · 1 citations
5Federated Learning to Safeguard Patients Data: A Medical Image Retrieval CaseOriginal Research · 2023

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