ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020
VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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
38 citing papers in PubMed, 58 citations in OpenAlex.
- Federated Time-to-Event Data Analysis Using Piecewise Exponential Regression in DataSHIELD.Statistics in medicine · 2026Article
- An international multi-centre study to develop and validate federated learning-based prognostic models for anal cancer.Nature communications · 2026Article
- Federated learning frameworks: quality and interoperability for biomedical research.NAR genomics and bioinformatics · 2026Review
- Ontology- and LLM-based data harmonization for federated learning in healthcare.Frontiers in digital health · 2026Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- Federated learning and Data Lakehouse for healthcare analytics: a knowledge transfer initiative between Germany and Tunisia.Frontiers in medicine · 2026Article
- Multi-Cohort Federated Learning Shows Synergy in Mortality Prediction for MRI-Based and Metabolomics-Based Age Scores.Journal of healthcare informatics research · 2025Article
- Predicting near-complete pathological response to (chemo)radiotherapy in patients with rectal cancer: A federated learning study.Medical physics · 2025Article
- Shechi: A Secure Distributed Computation Compiler Based on Multiparty Homomorphic Encryption.Proceedings. UNIX Security Symposium · 2025Article
- Linear Federated Learning for Outcome Prediction With Application to Hepatocellular Carcinoma Radiotherapy.JCO clinical cancer informatics · 2025Article
- Data Interoperability in Context: The Importance of Open-Source Implementations When Choosing Open Standards.Journal of medical Internet research · 2025Article
- A comprehensive experimental comparison between federated and centralized learning.Database : the journal of biological databases and curation · 2025Article
- Article
- The Applications of Machine Learning in the Management of Patients Undergoing Stem Cell Transplantation: Are We Ready?Cancers · 2025Review
- Verticox+: vertically distributed Cox proportional hazards model with improved privacy guarantees.Complex & intelligent systems · 2025Article
- FAIR Data Cube, a FAIR data infrastructure for integrated multi-omics data analysis.Journal of biomedical semantics · 2024Article
- MOLGENIS Armadillo: a lightweight server for federated analysis using DataSHIELD.Bioinformatics (Oxford, England) · 2024Article
- PHIRI: lessons for an extensive reuse of sensitive data in federated health research.European journal of public health · 2024Article
- A study on interoperability between two Personal Health Train infrastructures in leukodystrophy data analysis.Scientific data · 2024Article
- GEN-RWD Sandbox: bridging the gap between hospital data privacy and external research insights with distributed analytics.BMC medical informatics and decision making · 2024Article
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 at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Answering many of the research questions in the field of cancer informatics requires incorporating and centralizing data that are hosted by different parties. Federated Learning (FL) has emerged as a new approach in which a global model can be generated without disclosing private patient data by keeping them at their original location. Flexible, user-friendly, and robust infrastructures are crucial for bringing FL solutions to the day-to-day work of the cancer epidemiologist. In this paper, we present an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange, VANTAGE6. We provide a detailed description of its conceptual design, modular architecture, and components. We also show a few examples where VANTAGE6 has been successfully used in research on observational cancer data. Developing and deploying technology to support federated analyses - such as VANTAGE6 - will pave the way for the adoption and mainstream practice of this new approach for analyzing decentralized data.
Indexed as
Identifiers
33936462PMC8075508W3159002838What 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.