Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation
Authors: zengfeng Huang, Ziyue Huang, Yilei WANG, Ke Yi
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have also conducted experimental studies, which demonstrate the advantages of our method and confirm our theoretical findings. |
| Researcher Affiliation | Academia | Zengfeng Huang School of Data Science Fudan University huangzf@fudan.edu.cn Ziyue Huang Department of CSE HKUST zhuangbq@cse.ust.hk Yilei Wang Department of CSE HKUST ywanggq@cse.ust.hk Ke Yi Department of CSE HKUST yike@cse.ust.hk |
| Pseudocode | Yes | Algorithm 1 Scaling and Rounding (Sa R) input v Rd and a scaling factor F 1: u = v F 2: Randomized rounding: for j = 1, , d ˆuj = uj + 1, with probability uj uj uj , otherwise. 3: return ˆu |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used the MNIST [13] data set, uniformly or non-uniformly distributed across 10 clients. |
| Dataset Splits | No | The paper describes data distribution among clients ('distributed across 10 clients', '16 vectors, each held by a different client') but does not specify standard training, validation, and testing dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details (e.g., library or solver names with version numbers) needed to replicate the experiments. |
| Experiment Setup | Yes | The number of clusters and iterations is set to 10 and 30 respectively. [...] The number of clients is set to 100 and the number of iterations is set to 15. [...] we used different values of k (quantization level) for Suresh et al. s algorithm, k = 32 for less communication and k = 512 for less error, and other methods are tuned to achieve the same objective. |