On the public citation graph

Accessible Ecosystem for Clinical Research (Federated Learning for Everyone): Development and Usability Study

https://doi.org/10.2196/55496
0 signed · 59 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11787006_1openalex-snapshot
Differential Privacy
inferredcites →https://doi.org/10.1007/978-3-030-58951-6_24openalex-snapshot
Data Poisoning Attacks Against Federated Learning Systems
inferredcites →https://doi.org/10.1007/978-3-030-70604-3_5openalex-snapshot
PySyft: A Library for Easy Federated Learning
inferredcites →https://doi.org/10.1007/978-3-030-72792-5_30openalex-snapshot
FLSim: An Extensible and Reusable Simulation Framework for Federated Learning
inferredcites →https://doi.org/10.1007/s10618-023-00983-zopenalex-snapshot
Federated singular value decomposition for high-dimensional data
inferredcites →https://doi.org/10.1007/s11673-017-9810-0openalex-snapshot
Big Data and Health Research—The Governance Challenges in a Mixed Data Economy
inferredcites →https://doi.org/10.1007/s40801-021-00266-3openalex-snapshot
Current Status, Challenges, and Future Perspectives of Real-World Data and Real-World Evidence in Japan
inferredcites →https://doi.org/10.1016/j.inffus.2020.07.009openalex-snapshot
Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy
inferredcites →https://doi.org/10.1038/nrg3208openalex-snapshot
Mining electronic health records: towards better research applications and clinical care
inferredcites →https://doi.org/10.1038/sdata.2016.18openalex-snapshot
The FAIR Guiding Principles for scientific data management and stewardship
inferredcites →https://doi.org/10.1056/nejmsb1609216openalex-snapshot
Real-World Evidence — What Is It and What Can It Tell Us?
inferredcites →https://doi.org/10.1088/1361-6560/ac97d9openalex-snapshot
OpenFL: the open federated learning library
inferredcites →https://doi.org/10.1097/mlr.0b013e3181de9e17openalex-snapshot
Prediction Modeling Using EHR Data
inferredcites →https://doi.org/10.1109/jiot.2022.3143842openalex-snapshot
EasyFL: A Low-Code Federated Learning Platform for Dummies
inferredcites →https://doi.org/10.1136/amiajnl-2011-000681openalex-snapshot
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research
inferredcites →https://doi.org/10.1145/3394658openalex-snapshot
Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics
inferredcites →https://doi.org/10.1145/3404835.3462996openalex-snapshot
On the Privacy of Federated Pipelines
inferredcites →https://doi.org/10.1145/3524104openalex-snapshot
Blockchain-enabled Federated Learning: A Survey
inferredcites →https://doi.org/10.1145/3575810openalex-snapshot
A Survey on Soft Computing Techniques for Federated Learning- Applications, Challenges and Future Directions
inferredcites →https://doi.org/10.1145/3603165.3607364openalex-snapshot
Plato: An Open-Source Research Framework for Production Federated Learning
inferredcites →https://doi.org/10.1172/jci129197openalex-snapshot
Opportunities and challenges in using real-world data for health care
inferredcites →https://doi.org/10.1177/1352458520941485openalex-snapshot
COVID-19 in people with multiple sclerosis: A global data sharing initiative
inferredcites →https://doi.org/10.1186/2049-3258-72-35openalex-snapshot
The EPIRARE proposal of a set of indicators and common data elements for the European platform for rare disease registration
inferredcites →https://doi.org/10.1186/s12910-016-0153-xopenalex-snapshot
Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies
inferredcites →https://doi.org/10.1212/nxi.0000000000200021openalex-snapshot
Updated Results of the COVID-19 in MS Global Data Sharing Initiative
inferredcites →https://doi.org/10.1212/wnl.0000000000012753openalex-snapshot
Associations of Disease-Modifying Therapies With COVID-19 Severity in Multiple Sclerosis
inferredcites →https://doi.org/10.13063/2327-9214.1244openalex-snapshot
A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data
inferredcites →https://doi.org/10.1371/journal.pdig.0000033openalex-snapshot
A systematic review of federated learning applications for biomedical data
inferredcites →https://doi.org/10.1561/2200000083openalex-snapshot
Advances and Open Problems in Federated Learning
inferredcites →https://doi.org/10.1609/aaai.v37i13.26847openalex-snapshot
Industry-Scale Orchestrated Federated Learning for Drug Discovery
inferredcites →https://doi.org/10.2196/42621openalex-snapshot
The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach
inferredcites →https://doi.org/10.2196/48030openalex-snapshot
The Journey of Data Within a Global Data Sharing Initiative: A Federated 3-Layer Data Analysis Pipeline to Scale Up Multiple Sclerosis Research
inferredcites →https://doi.org/10.2196/publichealth.4939openalex-snapshot
Direct-to-Patient Research: Piloting a New Approach to Understanding Drug Safety During Pregnancy
inferredcites →https://doi.org/10.2471/blt.20.251561openalex-snapshot
Data sharing for novel coronavirus (COVID-19)
inferredcites →https://doi.org/10.29007/4bz1openalex-snapshot
Blockchain-Based platform for Distribution AI
inferredcites →https://doi.org/10.3390/ani12243450openalex-snapshot
A High-Precision Method for 100-Day-Old Classification of Chickens in Edge Computing Scenarios Based on Federated Computing
inferredcites →https://doi.org/10.3390/app12020734openalex-snapshot
Privacy-Preserving Federated Learning Using Homomorphic Encryption
inferredcites →https://doi.org/10.3390/ijerph15081644openalex-snapshot
Recommendations for Improving the Quality of Rare Disease Registries
inferredcites →https://doi.org/10.48550/arxiv.1807.00459openalex-snapshot
How To Backdoor Federated Learning
inferredcites →https://doi.org/10.48550/arxiv.2007.10987openalex-snapshot
IBM Federated Learning: an Enterprise Framework White Paper V0.1
inferredcites →https://doi.org/10.48550/arxiv.2007.13518openalex-snapshot
FedML: A Research Library and Benchmark for Federated Machine Learning
inferredcites →https://doi.org/10.48550/arxiv.2105.11367openalex-snapshot
FedScale: Benchmarking Model and System Performance of Federated Learning at Scale
inferredcites →https://doi.org/10.48550/arxiv.2107.11621openalex-snapshot
FedLab: A Flexible Federated Learning Framework
inferredcites →https://doi.org/10.48550/arxiv.2207.10308openalex-snapshot
UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks
inferredcites →https://doi.org/10.48550/arxiv.2210.04620openalex-snapshot
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
inferredcites →https://doi.org/10.48550/arxiv.2210.13291openalex-snapshot
NVIDIA FLARE: Federated Learning from Simulation to Real-World
inferredcites →https://doi.org/10.48550/arxiv.2311.00334openalex-snapshot
MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/9781394338726.ch11openalex-snapshot
Federated Learning and Its Impact on Decision‐Making in Smart Agriculture
inferred← citeshttps://doi.org/10.1038/s41746-025-01788-8openalex-snapshot
Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data
inferred← citeshttps://doi.org/10.1098/rsos.251318openalex-snapshot
Federated computing: information integration under sovereignty constraints
inferred← citeshttps://doi.org/10.1109/flta67013.2025.11336663openalex-snapshot
Applying Federated Learning to Block-Term Tensor Regression for Decentralised Data Analysis of Biomedical Data
inferred← citeshttps://doi.org/10.1109/uemcon67449.2025.11267589openalex-snapshot
Federated XGBoost-Powered Web Application for Safeguarding Personal Health Data in Hospitals and Clinics of Metropolitan Lima
inferred← citeshttps://doi.org/10.1145/3843774openalex-snapshot
Starfish-FL: Harnessing Agentic Federated Analytics
inferred← citeshttps://doi.org/10.2196/72874openalex-snapshot
Privacy-Preserving Glycemic Management in Type 1 Diabetes: Development and Validation of a Multiobjective Federated Reinforcement Learning Framework
inferred← citeshttps://doi.org/10.2196/89685openalex-snapshot
From Pilots to Services: Admission Mechanisms for Scalable Federated Health Data Collaboration - Design and Proof-of-Concept Study (Preprint)
inferred← citeshttps://doi.org/10.3389/fpubh.2025.1656603openalex-snapshot
AI-driven transformation of precision medicine: a comprehensive narrative review of key application areas, emerging paradigms, and future directions
inferred← citeshttps://doi.org/10.3390/bdcc9110275openalex-snapshot
LizAI XT—AI-Accelerated Management Platform for Complex Healthcare Data at Scale, Beyond EMR/EHR and Dashboards
inferred← citeshttps://doi.org/10.64898/2026.04.30.721881openalex-snapshot
NeuroFLAME: A Scalable, Privacy-Preserving Federated Learning Platform for Collaborative, Secure, and Reproducible Multi-Site Neuroimaging
inferred← citeshttps://doi.org/10.7759/cureus.73720openalex-snapshot
Addressing Challenges to Enhance Clinical Research in Portugal: Insights from the OncoT3 Expert Group Delphi Study
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.