Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints
Authors: Xinmeng Huang, Donghwan Lee, Edgar Dobriban, Hamed Hassani
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Further, we provide experimental results using both synthetic data and n-gram frequency estimation in the text domain, which corroborate its efficiency. |
| Researcher Affiliation | Academia | Xinmeng Huang , Donghwan Lee Graduate Group in Applied Mathematics and Computational Science University of Pennsylvania Philadelphia, PA 19104 {xinmengh, dh7401}@sas.upenn.edu Edgar Dobriban Department of Statistics and Data Science University of Pennsylvania Philadelphia, PA 19104 dobriban@wharton.upenn.edu Hamed Hassani Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia, PA 19104 hassani@seas.upenn.edu |
| Pseudocode | Yes | Algorithm 1 SHIFT: Sparse Heterogeneity Inspired collaboration and Fine-Tuning |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] |
| Open Datasets | Yes | We test SHIFT on synthetic data as well as the Shakespeare dataset [11]. The Shakespeare dataset was proposed as a benchmark for federated learning in [11]. |
| Dataset Splits | No | The paper mentions generating synthetic data and drawing datapoints for the Shakespeare dataset, but it does not specify explicit train/validation/test splits for reproduction. |
| Hardware Specification | No | The paper states "Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]", indicating no specific hardware details are provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments (e.g., programming language versions, library versions, or solver versions). |
| Experiment Setup | Yes | We set the threshold parameter α = ln(n) and the trimming proportion ω = 0.1. We set the dimension to d = 300 and run the simulation by varying n, T, s. We set the uniform distribution, p = (1/d, . . . , 1/d) as the central distribution. |