ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
Split Learning for Distributed Collaborative Training of Deep Learning Models in Health Informatics.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- PrivEdge: a hybrid split-federated learning framework for real-time electricity theft detection on edge nodes.Scientific reports · 2026Article
- Application of artificial intelligence in differentiating IgG4-related ophthalmic disease and orbital MALT lymphoma: a review of radiomics and deep learning advances.Frontiers in immunology · 2026Review
- Model centric collaboration reduces data sharing barriers in medical artificial intelligence.Discover artificial intelligence · 2026Review
- Clustered Federated Spatio-Temporal Graph Attention Networks for Skeleton-Based Action Recognition.Sensors (Basel, Switzerland) · 2025Article
- Privacy-preserving decentralized learning methods for biomedical applications.Computational and structural biotechnology journal · 2024Review
- STOP-HCV-HCC Program: Privacy-Preserving Innovation for Remote Data Access Analytics at Federally Qualified Health Centers in South Texas.Studies in health technology and informatics · 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
7 authors.
Funding
Abstract
Deep learning continues to rapidly evolve and is now demonstrating remarkable potential for numerous medical prediction tasks. However, realizing deep learning models that generalize across healthcare organizations is challenging. This is due, in part, to the inherent siloed nature of these organizations and patient privacy requirements. To address this problem, we illustrate how split learning can enable collaborative training of deep learning models across disparate and privately maintained health datasets, while keeping the original records and model parameters private. We introduce a new privacy-preserving distributed learning framework that offers a higher level of privacy compared to conventional federated learning. We use several biomedical imaging and electronic health record (EHR) datasets to show that deep learning models trained via split learning can achieve highly similar performance to their centralized and federated counterparts while greatly improving computational efficiency and reducing privacy risks.
Indexed as
Identifiers
38222326PMC10785879What 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.