ReviewHeliyon2024
Federated learning: Overview, strategies, applications, tools and future directions.
Review in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An Overview of Stress Analysis Based on Physiological Signals: Systematic Review of Open Datasets and Current Trends.Sensors (Basel, Switzerland) · 2025Pooled it
- Decoding the microbiome: artificial intelligence-targeted gut microenvironment breakthroughs in personalized cancer therapy.Gut microbes · 2026Review
- Navigating Healthcare AI Governance: the Comprehensive Algorithmic Oversight and Stewardship Framework for Risk and Equity.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Artificial Intelligence and Robotic Surgery in Lymphedema: A Scoping Review of Current Applications, Clinical Translation, and Evidence Gaps.Plastic and reconstructive surgery. Global open · 2026Article
- From data to decisions: The role of machine learning in precision ecological restoration.Ecological applications : a publication of the Ecological Society of America · 2026Review
- Eleven quick tips for Biomedical Federated Learning.PLoS computational biology · 2026Article
- Artificial intelligence and pediatric imaging data: ethical strategies for learning and collaboration.Pediatric radiology · 2026Review
- Advancing data protections for implantable brain-computer interfaces.Communications medicine · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Artificial Intelligence in Rare Diseases: Workflow-Integrated Precision Kidney Care.Clinics and practice · 2026Review
- Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help.JMIR AI · 2026Article
- Hierarchical proof of trust a Byzantine fault tolerant federated learning framework for industrial IoT applications.Scientific reports · 2026Article
- Advancements, challenges, and prospects of explainable AI in sleep disordered breathing.Sleep & breathing = Schlaf & Atmung · 2026Review
- From data to decisions: Toward a Biodiversity Monitoring Standards Framework.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Overcoming domain-specific challenges for artificial intelligence in abdominal oncology toward clinical translation.Discover oncology · 2026Review
- Federated Learning in Edge Computing: Vulnerabilities, Attacks, and Defenses-A Survey.Sensors (Basel, Switzerland) · 2026Review
- A new adaptive federated learning approach for privacy preserving UAV anomaly detection under non-IID distributions.Scientific reports · 2026Article
- Advances and challenges in single-cell RNA sequencing data analysis: a comprehensive review.Briefings in bioinformatics · 2026Review
- Privacy-aware learning analytics for exploratory behavior through federated and explainable AI.Frontiers in artificial intelligence · 2026Article
- Digital health in allergy care: current practices, evidence, and future prospects.Frontiers in allergy · 2026Review
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) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.
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.