Fusion of Personalized Federated Learning (PFL) with Differential Privacy (DP) Learning for Diagnosis of Arrhythmia Disease.
This paper presents a novel privacy-preserving architecture, a fusion of Federated Learning with Personalized Models and Differential Privacy (FLPMDP), for diagnosing arrhythmia from 12-lead electrocardiogram (ECG) signals. The architecture supports collaborative training in decentralized healthcare...
I tiakina i:
| Ngā kaituhi matua: | , , |
|---|---|
| Hōputu: | Artigo |
| Reo: | Inglês |
| I whakaputaina: |
Public Library of Science (PLoS)
2025-01-01
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| Rangatū: | PLoS ONE |
| Urunga tuihono: | https://doi.org/10.1371/journal.pone.0327108 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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