Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Information on health data is often shrouded in exclusivity and sharing of such information is normally difficult and time-locked for numerous reasons. Federated learning (FL) regularly accelerates knowledge sharing, yet it is not fully exploited to alleviate overall data exclusivity and time constraints. Here, we present a proof-of-concept for a public FL-based info portal of the STRONG AYA Initiative.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.3233/SHTI250407 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Learning from history for personalized federated learning.
Neural Netw
September 2025
College of Information Science, North China University of Technology, Beijing, China. Electronic address:
Personalized Federated Learning (pFL) has received extensive attentions, due to its ability to effectively process non-IID data distributed among different clients. However, most of the existing pFL methods focus on the collaboration between global and local models to enrich the personalization process, but ignoring a lot of valuable historical information, which represents the unique learning trajectory of each client. In this paper, we propose a pFL method called FedLFH, which introduces a tracking variable that allows each client to preserve historical information to facilitate personalization.
View Article and Find Full Text PDFApplications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.
RSC Med Chem
August 2025
Pharmaceutical Organic Chemistry Department, Faculty of Pharmacy, Suez Canal University 4.5 Km the Ring Road Ismailia 41522 Egypt.
Protein kinases are central regulators of cell signaling and play pivotal roles in a wide array of diseases, most notably cancer and autoimmune disorders. The clinical success of kinase inhibitors-such as imatinib and osimertinib-has firmly established kinases as valuable drug targets. However, the development of selective, potent inhibitors remains challenging due to the conserved nature of the ATP-binding site, off-target effects, resistance mutations, and patient-specific variability.
View Article and Find Full Text PDFEnhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.
Stat Biosci
August 2024
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of "gold standard" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions.
View Article and Find Full Text PDFA Tri-Factor Adaptive Federated Learning Framework for Parkinson's Disease Diagnosis via Multi-Source Facial Expression Analysis.
IEEE J Biomed Health Inform
September 2025
Early diagnosis of Parkinson's disease (PD) is crucial for timely treatment and disease management. Recent studies link PD to impaired facial muscle control, manifesting as "masked face" symptoms, offering a novel diagnostic approach through facial expression analysis. However, data privacy concerns and legal restrictions have resulted in significant "data silos", hindering data sharing and limiting the accuracy and generalizability of existing diagnostic models due to small, localized datasets.
View Article and Find Full Text PDF