Evidence map›Paper›PMID 39896264›Full record

ArticleComputational and structural biotechnology journal2025

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx.

Trung-Hieu Hoang, Jordan Fuhrman, Marcus Klarqvist, Miao Li, Pranshu Chaturvedi, Zilinghan Li, Kibaek Kim, Minseok Ryu, Ryan Chard, E A Huerta and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Trung-Hieu HoangDepartment of Electrical and Computer Engineering and Coordinated Science Laboratory, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA.
Jordan FuhrmanUniversity of Chicago, Chicago, IL, USA.
Marcus KlarqvistBroad Institute of Harvard and MIT, Cambridge, MA, USA.
Miao LiData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Pranshu ChaturvediData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Zilinghan LiData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Kibaek KimData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Minseok RyuSchool of Computing and Augmented Intelligence Arizona State University, Tempe, AZ, USA.
Ryan ChardData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
E A HuertaData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.
Maryellen GigerUniversity of Chicago, Chicago, IL, USA.
Ravi MadduriData Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. Specifically designed for this purpose, this work introduces APPFLx - a low-code, easy-to-use FL framework that enables easy setup, configuration, and running of FL experiments. APPFLx removes administrative boundaries of research organizations and healthcare systems while providing secure

Indexed as

Biomedical researchChest radiographsCloud computingCOVID-19 detectionElectrocardiogramFederated learning for scienceFunction as a serviceIdentity managementPrivacy-preserving

Identifiers

PMID39896264
PMCPMC11782895

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.