Universal EHR Federated Learning Framework
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Abstract (arXiv)
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide application of FL is restricted by two large challenges: the heterogeneous EHR systems, and the non-i.i.d. data characteristic. A recent research proposed a framework that unifies heterogeneous EHRs, named UniHPF. We attempt to address both the challenges simultaneously by combining UniHPF and FL. Our study is the first approach to unify heterogeneous EHRs into a single FL framework. This combination provides an average of 3.4% performance gain compared to local learning. We believe that our framework is practically applicable in the real-world FL.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 26 references that are in the map. First posted on arXiv 14 Nov 2022.
Builds on
15 of 26 references.
References · 15
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
- 2018Federated Optimization in Heterogeneous Networkscited by 2,971
- 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
- 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
- 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
- 2020The Future of Digital Health with Federated Learningcited by 625
- 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
- 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
- 2016RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanismcited by 344
- 2018Scalable and accurate deep learning for electronic health recordscited by 275
- 2015Doctor AI: Predicting Clinical Events via Recurrent Neural Networkscited by 205
- 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
3 more
- 2021Unifying Heterogeneous Electronic Health Records Systems via Text-Based Code Embeddingcited by 24
- 2022Towards the Practical Utility of Federated Learning in the Medical Domaincited by 9
- 2020A Comprehensive Evaluation of Multi-task Learning and Multi-task Pre-training on EHR Time-series Datacited by 8
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Citing papers per year: 2023: 1.
1 paper.