Summary of Fedccrl: Federated Domain Generalization with Cross-client Representation Learning, by Xinpeng Wang et al.
FedCCRL: Federated Domain Generalization with Cross-Client Representation Learning
by Xinpeng Wang, Yongxin Guo, Xiaoying Tang
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this paper, researchers tackle the challenge of training machine learning models that can effectively generalize to new domains in Federated Learning (FL) settings. Most existing Domain Generalization (DG) algorithms are not directly applicable to FL due to privacy constraints and limited data quantity at each client. To overcome these challenges, the authors propose a lightweight federated DG method called FedCCRL. This approach improves model generalization while preserving privacy and ensuring computational and communication efficiency. The method consists of two modules: cross-client feature extension and representation and prediction dual-stage alignment. Experimental results demonstrate that FedCCRL achieves state-of-the-art performance on various datasets, including PACS, OfficeHome, and miniDomainNet, across FL settings with varying numbers of clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists work to make computer models more flexible and able to learn from new types of data in a way that doesn’t compromise people’s privacy. Right now, many models are designed for specific tasks and can’t easily adapt to new situations. The researchers developed a new method called FedCCRL that allows models to learn from diverse sources of data without giving away sensitive information. They tested their approach on several datasets and found it performed better than other methods in similar situations. |
Keywords
» Artificial intelligence » Alignment » Domain generalization » Federated learning » Generalization » Machine learning
