ReviewComputational and structural biotechnology journal2025
Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.ACS pharmacology & translational science · 2026Review
- Federated Learning in Endodontics: A Framework for Privacy-Preserving Multicentre Artificial Intelligence.International dental journal · 2026Review
- Non-IID and aware federated intrusion detection with PBFT with secured model aggregation for multi institutional healthcare internet of things networks.Scientific reports · 2026Article
- ESR Innovation in Focus: AI in interventional radiology: existing applications and ongoing research.European radiology · 2026Article
- Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN.Scientific reports · 2026Article
- Diversity and Representation in Cardiovascular Research: Evidence Gaps, Emerging Models, and Policy Implications.International journal of environmental research and public health · 2026Review
- Beyond the hype: navigating the real-world applications of artificial intelligence (AI) in healthcare.Medical review (2021) · 2026Review
- The Role of Pharmacies in Providing Point-of-Care Services in the Era of Digital Health and Artificial Intelligence: An Updated Review of Technologies, Regulation and Socioeconomic Considerations.Healthcare (Basel, Switzerland) · 2026Review
- Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review.Frontiers in digital health · 2026Review
- Smart healthcare engineering: transnational understanding of atmospheric corrosion processes in healthcare facilities.Frontiers in public health · 2026Article
- From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.Computational and structural biotechnology journal · 2026Article
- Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction.Dermatopathology (Basel, Switzerland) · 2025Review
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated learning (FL)-a distributed machine learning that offers collaborative training of global models across multiple clients. FL has been considered for the design and development of many FL systems in various domains. Hence, we present a comprehensive survey and analysis of existing FL systems, drawing insights from more than 250 articles published in 2019-2024. Our review elucidates the functioning of FL systems, particularly in comparison with alternative distributed learning approaches. Considering the healthcare domain as an example, we define the building blocks of a typical FL healthcare system, including system architecture, federation scale, data partitioning, open-source frameworks, ML models, and aggregation algorithms. Furthermore, we identify and discuss key challenges associated with the design and implementation of FL systems within the healthcare sector while outlining the directions of future research. In general, through systematic categorization and analysis of existing FL systems, we offer insights to design efficient, accurate, and privacy-preserving healthcare applications using cutting-edge FL techniques.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.