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IPFL: Interpretable Federated Learning for Personalized Healthcare

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Název: IPFL: Interpretable Federated Learning for Personalized Healthcare
Autoři: Nijdam, Arthur A., Aminifar, Amir
Přispěvatelé: Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Electrical and Information Technology, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för elektro- och informationsteknik, Originator, Lund University, Faculty of Engineering, LTH, Departments at LTH, Department of Electrical and Information Technology, Secure and Networked Systems, Lunds universitet, Lunds Tekniska Högskola, Institutioner vid LTH, Institutionen för elektro- och informationsteknik, Säkerhets- och nätverkssystem, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Water, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Vatten, Originator
Zdroj: IEEE Access. 13:171156-171169
Témata: Natural Sciences, Computer and Information Sciences, Computer Sciences, Naturvetenskap, Data- och informationsvetenskap (Datateknik), Datavetenskap (Datalogi)
Popis: Federated Learning (FL) enables decentralized training of neural networks across multiple hospitals or patients while preserving data privacy. However, FL schemes typically assume data is independent and identically distributed (IID) while healthcare data can be highly heterogeneous. To address this, we propose Interpretable Personalized Federated Learning (IPFL), a novel framework that allows patients to selectively collaborate with others based on both validation performance and historical collaboration success. By directly inferring patient similarities from data, IPFL enables personalized model training without requiring assumptions about cluster structures or interpolation with a global model. We validate IPFL on two real-world healthcare tasks: epileptic seizure detection and cardiac arrhythmia detection, and show that it achieves state-of-the-art performance. Moreover, our analysis demonstrates that IPFL naturally leads to interpretable collaboration graphs: patients with similar disease characteristics tend to collaborate more frequently.
Přístupová URL adresa: https://doi.org/10.1109/ACCESS.2025.3608852
Databáze: SwePub
Popis
Abstrakt:Federated Learning (FL) enables decentralized training of neural networks across multiple hospitals or patients while preserving data privacy. However, FL schemes typically assume data is independent and identically distributed (IID) while healthcare data can be highly heterogeneous. To address this, we propose Interpretable Personalized Federated Learning (IPFL), a novel framework that allows patients to selectively collaborate with others based on both validation performance and historical collaboration success. By directly inferring patient similarities from data, IPFL enables personalized model training without requiring assumptions about cluster structures or interpolation with a global model. We validate IPFL on two real-world healthcare tasks: epileptic seizure detection and cardiac arrhythmia detection, and show that it achieves state-of-the-art performance. Moreover, our analysis demonstrates that IPFL naturally leads to interpretable collaboration graphs: patients with similar disease characteristics tend to collaborate more frequently.
ISSN:21693536
DOI:10.1109/ACCESS.2025.3608852