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22 papers · ranked by Valyu relevance

01
BbioRxiv8 May 2023
Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility
Dylan Martin, Sunitha Basodi, Sandeep Panta, Kelly Rootes-Murdy + 12 more
Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly…
91relevance
02
PPubMed19 Jun 2023
Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility
Dylan Martin, Sunitha Basodi, Sandeep Panta, Kelly Rootes-Murdy + 12 more
'Paul Prae' 'Anand D. Sarwate' 'Ross Kelly' 'Javier Romero' 'Bradley T. Baker' 'Harshvardhan Gazula' 'Jeremy Bockholt' 'Jessica A. Turner' 'Nathalia B. Esper' 'Alexandre R. Franco' 'Sergey Plis' 'Vince D. Calhoun'] Collaborative neuroimaging research is often hindered by technological, policy, administrative, and…
90relevance
03
PPubMed2 Dec 2024
MOLGENIS Armadillo: a lightweight server for federated analysis using DataSHIELD
Tim Cadman, Mariska K Slofstra, Marije A van der Geest, Demetris Avraam + 19 more
Globally, a vast amount of data has now been collected on human health, including in cohort studies (), national registries (), and biobanks (). Analysis of this data has the potential to answer important research questions, particularly questions about lifecourse risk factors that affect human health and development.…
89relevance
04
PPubMed30 May 2023
Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer
James Casaletto, Alexander Bernier, Robyn McDougall, Melissa S. Cline
Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to…
88relevance
05
CchemRxiv25 Nov 2025
Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)
Authors not listed
Next Generation Risk Assessment (NGRA) promotes animal-free, exposure-informed, and hypothesis-driven approaches to chemical safety assessment. In silico tools, such as quantitative structure-activity relationship (QSAR) models, are valuable new approach methodologies (NAMs) for use in NGRA. However, the practical…
87relevance
06
AarXiv19 Apr 2024
A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues
Zibo Wang, Haichao Ji, Yifei Zhu, Dan Wang + 1 more
Applications and Open Issues Authors: ['Zibo Wang' 'Haichao Ji' 'Yifei Zhu' 'Dan Wang' 'Zhu Han'] Abstract—The escalating influx of data generated by networked edge devices, coupled with the growing awareness of data privacy, has promoted a transformative shift in computing paradigms from centralized data processing to…
86relevance
07
BbioRxiv6 Jun 2021
Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort
James Casaletto, Michael Parsons, Charles Markello, Yusuke Iwasaki + 3 more
More than 40% of the germline variants in ClinVar today are variants of uncertain significance (VUS). These variants remain unclassified in part because the patient-level data needed for their interpretation is siloed. Federated analysis can overcome this problem by “bringing the code to the data”: analyzing the…
85relevance
08
PPubMed14 Nov 2024
Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach
Romain Jégou, Camille Bachot, Charles Monteil, Eric Boernert + 4 more
In the classical approach of clinical trials, data from healthcare providers is transferred and stored in a centralized environment for statistical analysis ([pone.0312697.g001]). Due to legal, ethical or informed consent restrictions that protect privacy of the patient, individual-level data cannot always be easily…
84relevance
09
PPubMed9 Dec 2024
Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform Federated privacy-protected meta- and mega-omic data analysis
Xavier Escriba-Montagut, Yannick Marcon, Augusto Anguita-Ruiz, Demetris Avraam + 6 more
'Demetris Avraam' 'Jose Urquiza' 'Andrei S. Morgan' 'Rebecca C. Wilson' 'Paul Burton' 'Juan R. Gonzalez' 'Gamze Gursoy'] The importance of maintaining data privacy and complying with regulatory requirements is highlighted especially when sharing omic data between different research centers. This challenge is even more…
84relevance
10
AarXiv13 Nov 2023
Federated statistical analysis: non-parametric testing and quantile estimation
Ori Becher, Mira Marcus-Kalish, David M. Steinberg
The age of big data has fueled expectations for accelerating learning. The availability of large data sets enables researchers to achieve more powerful statistical analyses and enhances the reliability of conclusions, which can be based on a broad collection of subjects. Often such data sets can be assembled only with…
83relevance
11
PPubMed28 Apr 2026
Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach
Megan Harmon, Na Li, Tolulope Sajobi, Jessalyn Holodinsky + 1 more
Introduction The disclosure of health data is governed by strict privacy regulations which significantly restrict the transfer of data across jurisdictions. These limitations restrict the scope of health research, particularly the ability to conduct studies that span multiple jurisdictions (e.g. provinces, states, or…
82relevance
12
BbioRxiv25 Feb 2021
Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption
David Froelicher, Juan R. Troncoso-Pastoriza, Jean Louis Raisaro, Michel A. Cuendet + 3 more
In biomedical research, real-world evidence, which is emerging as an indispensable complement of clinical trials, relies on access to large quantities of patient data that typically reside at separate healthcare institutions. Conventional approaches for centralizing those data are often not feasible due to privacy and…
82relevance
13
PPubMed24 Jul 2024
Centralized and Federated Models for the Analysis of Clinical Data
Ruowang Li, Joseph D. Romano, Yong Chen, Jason H. Moore
The progress of precision medicine research hinges on the gathering and analysis of extensive and diverse clinical datasets. With the continued expansion of modalities, scales, and sources of clinical datasets, it becomes imperative to devise methods for aggregating information from these varied sources to achieve a…
82relevance
14
BbioRxiv10 Dec 2024
FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis
Boris Muzellec, Ulysse Marteau-Ferey, Tanguy Marchand
Large-scale transcriptomic studies are often limited by data silos and risks of privacy leakage, which may lead to missed clinical insights. Meta-analysis methods may be used to aggregate local results, but they induce lower statistical power and are particularly sensitive to heterogeneous settings. A recent paradigm…
81relevance
15
AarXiv20 Sep 2024
Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework
Claudio Bonesana, Daniele Malpetti, Sandra Mitrović, Francesca Mangili + 1 more
Framework Authors: ['Claudio Bonesana' 'Daniele Malpetti' 'Sandra Mitrović' 'Francesca Mangili' 'Laura Azzimonti'] Abstract—We present Flotta1 , a Federated Learning framework designed to train machine learning models on sensitive data distributed across a multi-party consortium conducting research in contexts…
78relevance
16
AarXiv26 Sep 2024
FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration
Akira Imakura, Tetsuya Sakurai
privacy-preserving framework based on federated learning and data collaboration Authors: ['Akira Imakura' 'Tetsuya Sakurai'] Recently, federated learning has attracted much attention as a privacy-preserving integrated analysis that enables integrated analysis of data held by multiple institutions without sharing raw…
77relevance
17
AarXiv9 Dec 2025
FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection
Liao, Yihan, Keung, Jacky + 6 more
—Log-based anomaly detection (LAD) is critical for ensuring the reliability of large-scale distributed systems. However, most existing LAD approaches assume centralized training, which is often impractical due to privacy constraints and the decentralized nature of system logs. While federated learning (FL) offers a…
77relevance
18
BbioRxiv16 Jan 2023
Transfer learning in heterogeneous drug-target interaction predictions using federated boosting
Dániel Sándor, Péter Antal
In multitask federated learning, when small amounts of data are available, it can be harder to achieve proper predictive performance, especially if the clients’ tasks are different. However, task heterogeneity is common in modern Drug-Target interaction (DTI) prediction problems. As the data available for DTI tasks are…
74relevance
19
CchemRxiv6 Feb 2024
Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis
Junjie Hu, Xiangyu Li, Dan-Dan Liu, Shiyi Wang + 3 more
The combination of parametric quantum circuits and density matrix coding can significantly reduce the number of parameters in artificial neural networks. The reduction in the number of model parameters helps to improve the commu- nication efficiency when training deep learning models under federated learning…
68relevance
20
AarXiv7 Sep 2024
FedModule: A Modular Federated Learning Framework
Chuyi Chen, Zhe Zhang, Yanchao Zhao
—Federated learning (FL) has been widely adopted across various applications, such as healthcare, finance, and smart cities. However, as experimental scenarios become more complex, existing FL frameworks and benchmarks have struggled to keep pace. This paper introduces FedModule1 , a flexible and extensible FL…
66relevance
21
AarXiv20 Sep 2018
Federated AI for building AI Solutions across Multiple Agencies.
Dinesh Verma, Simon Julier, Greg Cirincione
The different sets of regulations existing for different agencies within the government make the task of creating AI enabled solutions in government difficult. Regulatory restrictions inhibit sharing of data across different agencies, which could be a significant impediment to training AI models. We discuss the…
66relevance
22
CchemRxiv28 May 2025
Open and FAIR Raman spectroscopy. Paving the way for artificial intelligence
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…
62relevance

Results for Federated analysis

Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility

MOLGENIS Armadillo: a lightweight server for federated analysis using DataSHIELD

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)

A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues

Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort

Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform Federated privacy-protected meta- and mega-omic data analysis

Federated statistical analysis: non-parametric testing and quantile estimation

Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

Centralized and Federated Models for the Analysis of Clinical Data

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework

FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration

FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis

FedModule: A Modular Federated Learning Framework

Federated AI for building AI Solutions across Multiple Agencies.

Open and FAIR Raman spectroscopy. Paving the way for artificial intelligence

Enhancing Collaborative Neuroimaging Research: Introducing COINSTAC Vaults for Federated Analysis and Reproducibility

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility

MOLGENIS Armadillo: a lightweight server for federated analysis using DataSHIELD

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

Federation of toxicological data resources for in silico New Approach Methodologies (NAMs)

A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues

Federated analysis of BRCA1 and BRCA2 variation in a Japanese cohort

Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach Capability and accuracy of usual statistical analyses in a real-world setting using a federated approach

Federated privacy-protected meta- and mega-omics data analysis in multi-center studies with a fully open-source analytic platform Federated privacy-protected meta- and mega-omic data analysis

Federated statistical analysis: non-parametric testing and quantile estimation

Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach

Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption

Centralized and Federated Models for the Analysis of Clinical Data

FedPyDESeq2: a federated framework for bulk RNA-seq differential expression analysis

Flotta: a Secure and Flexible Spark-inspired Federated Learning Framework

FedDCL: a federated data collaboration learning as a hybrid-type privacy-preserving framework based on federated learning and data collaboration

FedLAD: A Modular and Adaptive Testbed for Federated Log Anomaly Detection

Transfer learning in heterogeneous drug-target interaction predictions using federated boosting

Communication efficiency enhanced federated learning derived from quantum reinforcement learning for retrosynthesis

FedModule: A Modular Federated Learning Framework

Federated AI for building AI Solutions across Multiple Agencies.

Open and FAIR Raman spectroscopy. Paving the way for artificial intelligence