Summary of Distributionally Robust Alignment For Medical Federated Vision-language Pre-training Under Data Heterogeneity, by Zitao Shuai et al.
Distributionally Robust Alignment for Medical Federated Vision-Language Pre-training Under Data Heterogeneity
by Zitao Shuai, Chenwei Wu, Zhengxu Tang, Liyue Shen
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 paper proposes Federated Distributionally Robust Alignment (FedDRA), a framework for vision-language pre-training that addresses the challenge of biased cross-modal alignment due to client data heterogeneity in real-world scenarios. The authors utilize federated learning to scale up the dataset while preserving data privacy, and construct a distribution family encompassing potential test-time domains to optimize the pre-trained model’s performance across this space. To avoid over-fitting on client-specific information, the approach uses anchor representation from the global model to guide local training, and adopts a two-stage approach to tune deeper layers before updating the entire network. The authors demonstrate FedDRA’s effectiveness in enhancing medical federated VLP under data heterogeneity through extensive experiments on real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FedDra is a new way to train computers to understand images and words together, even when they come from different places. This can help doctors and hospitals use AI better. Right now, AI systems are really good at looking at pictures of skin cancer or reading x-rays, but they’re not as good at understanding what the doctor wants them to do with that information. FedDra tries to fix this by letting the computer learn from lots of different places, while keeping all the data safe and private. It’s like a game where the computer has to figure out how to understand pictures and words in different ways, so it can be really good at understanding what doctors want. |
Keywords
* Artificial intelligence * Alignment * Federated learning
