Summary of Fedgca: Global Consistent Augmentation Based Single-source Federated Domain Generalization, by Yuan Liu et al.
FedGCA: Global Consistent Augmentation Based Single-Source Federated Domain Generalization
by Yuan Liu, Shu Wang, Zhe Qu, Xingyu Li, Shichao Kan, Jianxin Wang
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 The Federated Domain Generalization (FedDG) aims to train a global model for generalization ability to unseen domains with multi-domain training samples. However, in federated learning networks, clients are often confined to a single non-IID domain due to sampling and temporal limitations. This limits the effectiveness of existing FedDG methods, referred to as the single-source FedDG (sFedDG) problem. To address this issue, the Federated Global Consistent Augmentation (FedGCA) method is introduced, which incorporates a style-complement module to augment data samples with diverse domain styles. FedGCA employs global guided semantic consistency and class consistency to ensure effective integration of augmented samples, mitigating inconsistencies from local semantics within individual clients and classes across multiple clients. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated Domain Generalization aims to make models better at dealing with new situations they haven’t seen before. The problem is that devices on a network are often only connected to one type of data, which makes it hard for the model to learn from all the different types of data. To fix this, researchers introduced a new method called Federated Global Consistent Augmentation (FedGCA). This method helps devices add diverse information from other types of data into their own, so that they can learn from each other better. |
Keywords
» Artificial intelligence » Domain generalization » Federated learning » Generalization » Semantics
