Summary of One-shot Collaborative Data Distillation, by William Holland et al.
One-Shot Collaborative Data Distillation
by William Holland, Chandra Thapa, Sarah Ali Siddiqui, Wei Shao, Seyit Camtepe
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 new approach to constructing synthetic datasets for machine learning, called CollabDM. The goal is to create high-quality, informative sets that can be used to train models efficiently and reduce data sharing costs. To do this, the authors introduce a collaborative distillation technique that captures the global distribution of the data and requires only one round of communication between clients and servers. This approach outperforms state-of-the-art methods in distributed learning environments and has promising practical applications, such as attack detection in 5G networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn how to make fake datasets for machines that can help them get smarter faster. Usually, making these fake datasets is hard because the computers have different information. To fix this problem, the authors created a new way of working together called CollabDM. It makes sure all the computers are working with the same information and only needs one round of talking between them. This new method works better than other methods and can be used to help detect attacks on 5G networks. |
Keywords
» Artificial intelligence » Distillation » Machine learning
