Summary of Multi-source Collaborative Gradient Discrepancy Minimization For Federated Domain Generalization, by Yikang Wei and Yahong Han
Multi-Source Collaborative Gradient Discrepancy Minimization for Federated Domain Generalization
by Yikang Wei, Yahong Han
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 a novel approach called Multi-source Collaborative Gradient Discrepancy Minimization (MCGDM) for Federated Domain Generalization. In this context, multiple decentralized source domains provide isolated data due to privacy concerns, making it challenging to bridge the domain gap. To address this issue, MCGDM combines intra-domain and inter-domain gradient matching techniques to learn a domain-invariant model that generalizes well across unseen target domains. The method is further extended for Federated Domain Adaptation by fine-tuning the target model on pseudo-labeled data. Experimental results show significant performance gains over state-of-the-art methods in both tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers learn from different groups of information, but keeps that information private and separate. They want a way to mix this information together so it can be used in new situations. The authors came up with a special method called MCGDM that helps them do this. It’s like a puzzle where they find the right connections between similar pieces of information from different groups. This makes the computer’s learning more accurate and useful for new tasks. |
Keywords
» Artificial intelligence » Domain adaptation » Domain generalization » Fine tuning
