Summary of Federated Unsupervised Domain Generalization Using Global and Local Alignment Of Gradients, by Farhad Pourpanah et al.
Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients
by Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany, Michael Greenspan, Ali Etemad
First submitted to arxiv on: 25 May 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 The proposed method, FedGaLA, addresses the problem of federated domain generalization in an unsupervised setting by aligning gradients at both client and server levels. This facilitates the model’s ability to generalize to new domains. The approach consists of client-level gradient alignment to encourage domain-invariant feature learning and global gradient alignment at the server to obtain a more generalized aggregated model. Experimental results on four multi-domain datasets, including PACS, OfficeHome, DomainNet, and TerraInc, demonstrate the effectiveness of FedGaLA, outperforming comparable baselines. Ablation and sensitivity studies highlight the impact of different components and parameters in the approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, scientists have developed a new way to help machines learn from different types of data without being specifically trained for each type. This is useful when we want to apply what we’ve learned to new situations. The new method, called FedGaLA, makes this happen by adjusting how the machine learns in two ways: at individual “clients” (like a smartphone) and at the central location where all the data comes together. By doing so, it can learn general patterns that work across different types of data. |
Keywords
» Artificial intelligence » Alignment » Domain generalization » Unsupervised
