Summary of Federated T-sne and Umap For Distributed Data Visualization, by Dong Qiao et al.
Federated t-SNE and UMAP for Distributed Data Visualization
by Dong Qiao, Xinxian Ma, Jicong Fan
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 novel techniques for high-dimensional data visualization in a distributed setting, specifically addressing the challenges posed by big data’s security and privacy concerns. Building upon existing methods like t-SNE and UMAP, Fed-tSNE and Fed-UMAP enable visualization without exchanging data between clients or servers. The approach involves implicitly learning distribution information through federated learning and estimating global distance matrices for t-SNE and UMAP. To enhance data privacy, the authors also introduce Fed-tSNE+ and Fed-UMAP+, along with theoretical guarantees of optimization convergence, distance estimation, and differential privacy. Experimental results demonstrate that the accuracy drops of these federated algorithms are minimal compared to their original counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a huge amount of data spread across many places, and you want to understand how it’s connected. But, if this data is private or secure, you can’t just send it all to one place for analysis. This paper solves that problem by creating new ways to visualize high-dimensional data without sharing it. They took existing methods like t-SNE and UMAP and adapted them for distributed data, making sure the results are accurate and private. The authors also showed that their approach works well in practice with real-world datasets. |
Keywords
» Artificial intelligence » Federated learning » Optimization » Tsne » Umap
