Federated t-SNE and UMAP for Distributed Data Visualization
Authors: Dong Qiao, Xinxian Ma, Jicong Fan
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple datasets demonstrate that, compared to the original algorithms, the accuracy drops of our federated algorithms are tiny. |
| Researcher Affiliation | Academia | School of Data Science, The Chinese University of Hong Kong, Shenzhen, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Federated Distribution Learning |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for the described methodology. |
| Open Datasets | Yes | We applied the proposed Fed-t SNE and Fed-UMAP methods to the MNIST and Fashion-MNIST datasets, with m X = 40, 000, and set n Y = 500. Additionally, "We utilized three datasets MNIST, COIL-20, and Mice Protein (detailed in Appendix) to evaluate the effectiveness of our Fed-Spe Clust." |
| Dataset Splits | Yes | We designed the experiment with 10 clients, where IID (independent and identically distributed) refers to each client s data being randomly sampled from the MNIST dataset, thus including all classes. In contrast, non-IID means that each client s data contains only a single class. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | We applied the proposed Fed-t SNE and Fed-UMAP methods to the MNIST and Fashion-MNIST datasets, with m X = 40, 000, and set n Y = 500. We designed the experiment with 10 clients, where IID (independent and identically distributed) refers to each client s data being randomly sampled from the MNIST dataset, thus including all classes. In contrast, non-IID means that each client s data contains only a single class. In Figure 2, the relevant metrics reached convergence after approximately 50 epochs. The noise level β controls the scale of noise, with each element of noise E being drawn from N(0, β2sd2( fp(Yp))). |