FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Authors: Ziwei Li, Xiaoqi Wang, Hong-You Chen, Han Wei Shen, Wei-Lun (Harry) Chao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on both synthetic and real-world datasets. The results demonstrate that our FEDNE can effectively preserve the neighborhood data structures and enhance the alignment in the global embedding space compared to several baseline methods. |
| Researcher Affiliation | Academia | Ziwei Li, Xiaoqi Wang, Hong-You Chen, Han-Wei Shen, Wei-Lun Chao The Ohio State University {li.5326, wang.5502, chen.9301, shen.94, chao.209}@osu.edu |
| Pseudocode | No | The paper does not contain explicitly labeled pseudocode or algorithm blocks. It describes the overall framework with a diagram and accompanying text. |
| Open Source Code | Yes | The main implementations will be available soon under https://github.com/Ziwei-0129/Fed NE. |
| Open Datasets | Yes | We conduct experimental studies on four benchmark datasets that have been widely used in the field of dimensionality reduction [35, 55]: MNIST [26], Fashion-MNIST [48], mouse retina single-cell transcriptomes [32], and CIFAR-10 [25]. |
| Dataset Splits | Yes | We assess the quality of data embeddings by analyzing the input high-dimensional data points and their corresponding 2D positions [14]. First, to evaluate the preservation of neighborhood structures, we compute trustworthiness and continuity scores. |
| Hardware Specification | Yes | We conducted experiments using MNIST with 20 clients on a server with 4 NVIDIA Ge Force RTX 2080 Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Adam optimizer' but does not specify its version or the version of any other libraries or programming languages used. |
| Experiment Setup | Yes | In the experiments of MNIST and Fashion-MNIST datasets, we use Adam optimizer with a learning rate of 0.001 and a batch size of 512 (i.e., the number of edges in the k NN graphs not the number of data instances). The learning rate was decreased by 0.1 at 30% and 60% of the total rounds. For the experiments with the single-cell RNA-Seq and CIFAR-10 dataset, the learning rate was initially set to 1 10 4. For negative sampling, we fix the number of non-neighboring data points sampled per edge to be 5 (b = 5). |