ReviewComputational and structural biotechnology journal2024
Privacy-preserving decentralized learning methods for biomedical applications.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN.Scientific reports · 2026Article
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- From Biomedical Datasets to Fairness-Aware Recommendations: An Integrated Data Orchestration Pipeline for Binary Clinical Predictions.Computational and structural biotechnology journal · 2026Article
- MedChat: a fully offline multimodal AI system for privacy-preserving clinical anamnesis.Frontiers in artificial intelligence · 2026Article
- Privacy-preserving federated unsupervised domain adaptation with application to age prediction from DNA methylation data.Bioinformatics (Oxford, England) · 2025Article
- Mamba-fusion for privacy-preserving disease prediction.Scientific reports · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- gLinDA: A privacy-preserving, swarm learning toolbox for differential abundance analysis of microbiomes.Computational and structural biotechnology journal · 2025Article
- From bites to bytes: understanding how and why individual malaria risk varies using artificial intelligence and causal inference.Frontiers in genetics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, decentralized machine learning has emerged as a significant advancement in biomedical applications, offering robust solutions for data privacy, security, and collaboration across diverse healthcare environments. In this review, we examine various decentralized learning methodologies, including federated learning, split learning, swarm learning, gossip learning, edge learning, and some of their applications in the biomedical field. We delve into the underlying principles, network topologies, and communication strategies of each approach, highlighting their advantages and limitations. Ultimately, the selection of a suitable method should be based on specific needs, infrastructures, and computational capabilities.
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.