Summary of Cl3: a Collaborative Learning Framework For the Medical Data Ensuring Data Privacy in the Hyperconnected Environment, by Mohamamd Zavid Parvez et al.
CL3: A Collaborative Learning Framework for the Medical Data Ensuring Data Privacy in the Hyperconnected Environment
by Mohamamd Zavid Parvez, Rafiqul Islam, Md Zahidul Islam
First submitted to arxiv on: 10 Oct 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 This study proposes a collaborative learning framework, called CL3, to detect COVID-19 using chest X-ray images while ensuring patient data privacy. The framework combines transfer, federated, and incremental learning to generate efficient and scalable models that adapt to new medical data without forgetting previously learned information. The study employs a pre-trained model as the starting global model, integrates local models from different medical institutes, and constructs a new global model to adapt to any data drift. Experimental results show that CL3 achieves a global accuracy of 89.99% using Xception with a batch size of 16 after six federated communication rounds. The framework’s reproducibility is ensured through a demo available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to work together and share information to help detect COVID-19 from chest X-ray images while keeping patient data safe. They combined three types of learning methods: transfer, federated, and incremental learning. This allows the model to learn quickly, adapt to new data, and remember what it already knows. The team tested their approach and found that it was very accurate, achieving 89.99% correct diagnoses after training for a few rounds. |
