Summary of Enhancing Federated Domain Adaptation with Multi-domain Prototype-based Federated Fine-tuning, by Jingyuan Zhang et al.
Enhancing Federated Domain Adaptation with Multi-Domain Prototype-Based Federated Fine-Tuning
by Jingyuan Zhang, Yiyang Duan, Shuaicheng Niu, Yang Cao, Wei Yang Bryan Lim
First submitted to arxiv on: 10 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 The proposed Federated Domain Adaptation (FDA) framework addresses the challenge of data heterogeneity in FL by introducing a novel approach called Multi-domain Prototype-based Federated Fine-Tuning (MPFT). This method fine-tunes a pre-trained model using multi-domain prototypes, enabling supervised learning on the server and deriving a globally optimized adapter that is distributed to local clients without compromising data privacy. The MPFT framework significantly improves both in-domain and out-of-domain accuracy over conventional methods, enhancing knowledge preservation and adaptation in FDA. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new way to train machine learning models across different devices or organizations, called Federated Domain Adaptation (FDA). This is important because it allows for better model performance while keeping the data private. The challenge is that the data can be very different from one device to another. To address this, they propose a framework called MPFT, which uses prototypes to fine-tune models and make them more accurate. |
Keywords
» Artificial intelligence » Domain adaptation » Fine tuning » Machine learning » Supervised
