ArticlePatterns (New York, N.Y.)2024
Unified fair federated learning for digital healthcare.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Eleven quick tips for Biomedical Federated Learning.PLoS computational biology · 2026Article
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.ACM transactions on intelligent systems and technology · 2026Article
- Federated Learning in Healthcare Ethics: A Systematic Review of Privacy-Preserving and Equitable Medical AI.Healthcare (Basel, Switzerland) · 2026Review
- Model centric collaboration reduces data sharing barriers in medical artificial intelligence.Discover artificial intelligence · 2026Review
- Federated Learning in Public Health: A Systematic Review of Decentralized, Equitable, and Secure Disease Prevention Approaches.Healthcare (Basel, Switzerland) · 2025Review
- FairFML: fair federated machine learning with a case study on reducing gender disparities in cardiac arrest outcome prediction.npj health systems · 2025Article
- A scoping review and evidence gap analysis of clinical AI fairness.NPJ digital medicine · 2025Article
- Revolutionizing healthcare data analytics with federated learning: A comprehensive survey of applications, systems, and future directions.Computational and structural biotechnology journal · 2025Review
- Unified fair federated learning for digital healthcare.Patterns (New York, N.Y.) · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated learning (FL) is a promising approach for healthcare institutions to train high-quality medical models collaboratively while protecting sensitive data privacy. However, FL models encounter fairness issues at diverse levels, leading to performance disparities across different subpopulations. To address this, we propose Federated Learning with Unified Fairness Objective (FedUFO), a unified framework consolidating diverse fairness levels within FL. By leveraging distributionally robust optimization and a unified uncertainty set, it ensures consistent performance across all subpopulations and enhances the overall efficacy of FL in healthcare and other domains while maintaining accuracy levels comparable with those of existing methods. Our model was validated by applying it to four digital healthcare tasks using real-world datasets in federated settings. Our collaborative machine learning paradigm not only promotes artificial intelligence in digital healthcare but also fosters social equity by embodying fairness.
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.