Summary of Comparing Federated Stochastic Gradient Descent and Federated Averaging For Predicting Hospital Length Of Stay, by Mehmet Yigit Balik
Comparing Federated Stochastic Gradient Descent and Federated Averaging for Predicting Hospital Length of Stay
by Mehmet Yigit Balik
First submitted to arxiv on: 17 Jul 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 paper proposes a novel approach to predict hospital length of stay (LOS) reliably, focusing on decentralized data sources from different hospitals. The authors model this problem as an empirical graph where nodes represent hospitals, enabling collaborative model training without sharing sensitive patient data. A local model is trained using generalized total variation minimization (GTVMin), and two federated learning optimization algorithms, federated stochastic gradient descent (FedSGD) and federated averaging (FedAVG), are compared. The results demonstrate the effectiveness of federated learning in predicting hospital LOS while addressing privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding a way to predict how long patients will stay in the hospital without sharing their personal information. This is important because hospitals need to make sure they have enough beds and staff for all the patients. The authors came up with a new way of doing this by creating a special kind of map that shows all the different hospitals. They can then use this map to train models that predict how long patients will stay in each hospital without sharing any personal information. This helps keep patient data private, which is important for keeping people’s medical records safe. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Stochastic gradient descent
