Summary of Ccva-fl: Cross-client Variations Adaptive Federated Learning For Medical Imaging, by Sunny Gupta and Amit Sethi
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging
by Sunny Gupta, Amit Sethi
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Cross-Client Variations Adaptive Federated Learning (CCVA-FL) addresses the challenges of training medical image models in a federated learning setting. CCVA-FL minimizes cross-client variations by transforming images into a common feature space, using Scalable Diffusion Models with Transformers (DiT). The method involves expert annotation of local images, synthetic image generation, and image-to-image translation for each client. This approach outperforms Vanilla Federated Averaging while maintaining privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to train medical image models using data from different sources without sharing the original images. They want to make sure that the model is good at recognizing patterns in images from all these different sources. To do this, they create synthetic images that are like the real images but not actually real. Then, they use a special kind of machine learning called federated learning to train a model on all these images. |
Keywords
» Artificial intelligence » Federated learning » Image generation » Machine learning » Translation
