Summary of On the Effects Of Similarity Metrics in Decentralized Deep Learning Under Distributional Shift, by Edvin Listo Zec et al.
On the effects of similarity metrics in decentralized deep learning under distributional shift
by Edvin Listo Zec, Tom Hagander, Eric Ihre-Thomason, Sarunas Girdzijauskas
First submitted to arxiv on: 16 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the challenges of Decentralized Learning (DL) when organizations or users collaborate to enhance local deep learning models without direct data exchange. The authors investigate various similarity metrics in DL for identifying compatible collaborators and aggregating models, conducting an empirical analysis across multiple datasets with distribution shifts. They examine the effectiveness of these metrics in facilitating effective collaboration and provide insights into their strengths and limitations. Their research contributes to the development of robust DL methods for privacy-preserving model merging. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a situation where different organizations or people want to work together to make artificial intelligence models better without sharing their data directly. This is called Decentralized Learning (DL). The problem is that these groups have different kinds of data, and finding the right collaborators to combine their efforts is tricky. In this paper, scientists looked at various ways to measure how similar these groups are, so they can work together effectively. They tested these methods on different datasets with different types of information and found what works best. Their research helps us develop better methods for making AI models better without sharing sensitive data. |
Keywords
» Artificial intelligence » Deep learning
