Summary of Client-centered Federated Learning For Heterogeneous Ehrs: Use Fewer Participants to Achieve the Same Performance, by Jiyoun Kim et al.
Client-Centered Federated Learning for Heterogeneous EHRs: Use Fewer Participants to Achieve the Same Performance
by Jiyoun Kim, Junu Kim, Kyunghoon Hur, Edward Choi
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research introduces EHRFL, a federated learning framework that develops client-specific models using electronic health records (EHRs). Unlike traditional centralized approaches, EHRFL enables training on data from multiple institutions while preserving patient privacy and complying with regulatory constraints. The framework addresses two key challenges: enabling federated learning across clients with heterogeneous EHR systems and reducing the cost of federated learning by selecting suitable participating clients using averaged patient embeddings. Experiment results on multiple open-source EHR datasets demonstrate the effectiveness of EHRFL in addressing these challenges, establishing it as a practical solution for building client-specific models in federated learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train AI models on healthcare data without breaking privacy rules. The problem is that most research focuses on creating one big model that works everywhere, instead of making models tailored to specific hospitals or clinics. This paper introduces EHRFL, a new framework that does exactly that. It uses electronic health records (EHRs) and helps doctors make better predictions by training AI models just for their hospital. The framework has two main parts: one that lets different hospitals with different EHR systems work together, and another that picks the right hospitals to participate in the learning process. The results show that this approach works well on real-world data sets. |
Keywords
» Artificial intelligence » Federated learning
