ArticlePatterns (New York, N.Y.)2022
Privacy-preserving federated neural network learning for disease-associated cell classification.
Article in Patterns (New York, N.Y.), 2022. 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.
- Clifti-GPT: privacy-preserving federated fine-tuning and transferable inference of foundation models on clinical single-cell data.BioData mining · 2026Article
- Machine Learning Enhanced Quantum-Safe Encryption: A Novel Optimisation Framework.Sensors (Basel, Switzerland) · 2026Article
- Machine learning-driven adaptive parameter selection for homomorphic encryption in edge computing.Scientific reports · 2026Article
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- PRISM: privacy-preserving rare disease analysis using fully homomorphic encryption.Bioinformatics (Oxford, England) · 2025Article
- FedscGen: privacy-preserving federated batch effect correction of single-cell RNA sequencing data.Genome biology · 2025Article
- Secure and federated genome-wide association studies for biobank-scale datasets.Nature genetics · 2025Article
- Privacy-Enhancing Technologies in Biomedical Data Science.Annual review of biomedical data science · 2024Review
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- Encrypted federated learning for secure decentralized collaboration in cancer image analysis.Medical image analysis · 2024Article
- Unified fair federated learning for digital healthcare.Patterns (New York, N.Y.) · 2024Article
- scFed: federated learning for cell type classification with scRNA-seq.Briefings in bioinformatics · 2023Article
- Scalable and Privacy-Preserving Federated Principal Component Analysis.Proceedings. IEEE Symposium on Security and Privacy · 2023Article
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
Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing or centralizing the data of different healthcare institutions is, however, unfeasible or prohibitively difficult due to privacy regulations. In this work, we address this problem by using a privacy-preserving federated learning-based approach,
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.