Summary of Federated Domain Generalization Via Prompt Learning and Aggregation, by Shuai Gong et al.
Federated Domain Generalization via Prompt Learning and Aggregation
by Shuai Gong, Chaoran Cui, Chunyun Zhang, Wenna Wang, Xiushan Nie, Lei Zhu
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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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 to federated domain generalization (FedDG), which aims to improve global model generalization in unseen domains while addressing data heterogeneity under privacy-preserving constraints. The authors introduce prompt learning to adapt pre-trained vision-language models (VLMs) in the FedDG scenario, leveraging locally learned prompts as a more secure bridge to facilitate knowledge transfer among clients. They propose a framework called PLAN (Prompt Learning and AggregatioN), which consists of two training stages: local prompt learning using client data and global prompt learning through lightweight attention-based aggregators. The authors demonstrate the superior generalization ability of their approach across four benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how computers learn to recognize pictures and words in different situations, while keeping information private. It’s like a secret code that lets devices share knowledge without sharing sensitive data. The researchers use “prompts” – short messages – to help computers understand what they’re seeing and learning from. They show that this approach is better than others at recognizing things it hasn’t seen before. This can be useful in many areas, such as medical imaging or self-driving cars. |
Keywords
» Artificial intelligence » Attention » Domain generalization » Generalization » Prompt
