paperarXivTrust 82 · PrimaryPublished 1mo agoLive · 1mo ago

Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction

Cardiovascular disease risk prediction models often rely on data from a single institution or centrally pooled datasets. Extending these models across institutions could be limited by privacy regulations and constraints on sharing patient-level data. Federated learning enables collaborative model development without transferring sensitive patient data, but its application in healthcare remains challenging because datasets often differ in size, population characteristics, and outcome definitions. In this study, we present a federated deep learning approach for privacy-preserving cardiovascular

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Paper → model → repo connections mined from source citations (Tier-1 exact match).

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PossiblePossibly related (embedding) · 47%Long-horizon local optimization and regularized knowledge improve personalized federated recommendation - Bioengineer.org →
LinkedLinked via arxiv author · 85%Hyunho Mo →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Djura Smits →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Mahlet A. Birhanu →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Maarten J. G. Leening →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Daniel Bos →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Pim van der Harst →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”
LinkedLinked via arxiv author · 85%Esther E. Bron →
“Federated Deep Learning for Privacy-Preserving Cardiovascular Disease Risk Prediction”

Covers (incoming)

newsLong-horizon local optimization and regularized knowledge improve personalized federated recommendation - Bioengineer.org newsFederated Learning Could Train AI Language Models Without Sharing Private Data - Bioengineer.org

authored (incoming)

personHyunho Mo personDjura Smits personMahlet A. Birhanu personMaarten J. G. Leening personDaniel Bos personPim van der Harst personEsther E. Bron

Implements (incoming)

repoaymericdamien/TopDeepLearning

Related across the graph

personDaniel Bos personEsther E. Bron personPim van der Harst personHyunho Mo newsLong-horizon local optimization and regularized knowledge improve personalized federated recommendation - Bioengineer.org personDjura Smits repoaymericdamien/TopDeepLearning newsFederated Learning Could Train AI Language Models Without Sharing Private Data - Bioengineer.org personMaarten J. G. Leening personMahlet A. Birhanu

Topics

cs.LG