ReviewSensors (Basel, Switzerland)2023
Reviewing Federated Machine Learning and Its Use in Diseases Prediction.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Federated Learning-Based Model for Predicting Mortality: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Wearable Devices and Machine Learning in Cardiovascular Monitoring: Current Evidence and Future Directions for Precision Medicine.Journal of personalized medicine · 2026Review
- Data Privacy, Ownership, and Secondary Use of Clinical Data Generated by Continuous Glucose Monitors and Mobile Health Applications: A Review.Journal of diabetes science and technology · 2026Review
- Interpretable Semi-federated Learning for Multimodal Cardiac Imaging and Risk Stratification: A Privacy-Preserving Framework.Journal of imaging informatics in medicine · 2026Article
- Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.Journal of imaging informatics in medicine · 2026Review
- Review
- How AI-based techniques for brain tumour diagnosis can address data variability.Frontiers in bioinformatics · 2026Article
- Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness.Frontiers in cardiovascular medicine · 2026Review
- Development of a prediction model for peptic ulcer disease using the fusion of a feature selection strategy and ensemble algorithms.BMC gastroenterology · 2025Article
- A quantitative and qualitative assessment of differential privacy's ability to support collaborative research using a real-world data analysis.JAMIA open · 2025Article
- A personalized federated learning-based glucose prediction algorithm for high-risk glycemic excursion regions in type 1 diabetes.Scientific reports · 2025Article
- In Search of Ideal Solutions for Cancer Diagnosis: From Conventional Methods to Protein Biomarkers in Liquid Biopsy.Proteomes · 2025Review
- A scoping review of the governance of federated learning in healthcare.NPJ digital medicine · 2025Article
- Federated learning with integrated attention multiscale model for brain tumor segmentation.Scientific reports · 2025Article
- Deep learning-driven approach for cataract management: towards precise identification and predictive analytics.Frontiers in cell and developmental biology · 2025Review
- U-FDL-PPE: a unified federated deep learning framework with privacy-preserving explainability for early and accurate viral disease prediction.Frontiers in radiology · 2025Article
- Efficient federated learning via aggregation of base models.PloS one · 2025Article
- Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration.Healthcare (Basel, Switzerland) · 2024Review
- Sub-clustering based recommendation system for stroke patient: Identification of a specific drug class for a given patient.Computers in biology and medicine · 2024Article
- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
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
Abstract
Machine learning (ML) has succeeded in improving our daily routines by enabling automation and improved decision making in a variety of industries such as healthcare, finance, and transportation, resulting in increased efficiency and production. However, the development and widespread use of this technology has been significantly hampered by concerns about data privacy, confidentiality, and sensitivity, particularly in healthcare and finance. The "data hunger" of ML describes how additional data can increase performance and accuracy, which is why this question arises. Federated learning (FL) has emerged as a technology that helps solve the privacy problem by eliminating the need to send data to a primary server and collect it where it is processed and the model is trained. To maintain privacy and improve model performance, FL shares parameters rather than data during training, in contrast to the typical ML practice of sending user data during model development. Although FL is still in its infancy, there are already applications in various industries such as healthcare, finance, transportation, and others. In addition, 32% of companies have implemented or plan to implement federated learning in the next 12-24 months, according to the latest figures from KPMG, which forecasts an increase in investment in this area from USD 107 million in 2020 to USD 538 million in 2025. In this context, this article reviews federated learning, describes it technically, differentiates it from other technologies, and discusses current FL aggregation algorithms. It also discusses the use of FL in the diagnosis of cardiovascular disease, diabetes, and cancer. Finally, the problems hindering progress in this area and future strategies to overcome these limitations are discussed in detail.
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.