Federated learning as a catalyst for digital healthcare innovations is a research paper published in Patterns (2024). On theSindex it has a DataRank of 0.425. It has been cited 6 times, with 6 citing works in its 1-hop citation network.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.292
From this paper's citation signal
Citation Network Contribution
0.133
From 2 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 6 citers.
National Cancer Institute
Grant: U01CA242871
National Cancer Institute
Grant: U24CA279629
UK Research and Innovation
Grant: MR/V023799/1
Synergistic Inverse Problems Omni-Solver for Expeditious High Quality Multimodal Cardiovascular MRI via Deep Compressive Sensing and Data Coalescing
National Institutes of Health
Grant: 5U24CA279629-02
Privacy-Aware Federated Learning for Breast Cancer Risk Assessment
National Institutes of Health
Grant: 5U01CA242871-02
The Federated Tumor Segmentation (FeTS) platform: An intuitive tool facilitating secure multi-institutional collaboration
FWCI
1.39
Citation Percentile
0.8%
Influential Citations
1
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals