Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Authors: Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,222 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,788 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude our work by showing how differential privacy can be added to our algorithm and verifying its superior performance through extensive experiments conducted on large-scale datasets. |
| Researcher Affiliation | Collaboration | Jingfeng Wu Johns Hopkins University EMAIL Wennan Zhu Google Research EMAIL Peter Kairouz Google Research EMAIL Vladimir Braverman Rice University EMAIL |
| Pseudocode | Yes | Algorithm 1 COUNT SKETCH FOR FEDERATED FREQUENCY ESTIMATION |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code will be made available. |
| Open Datasets | Yes | In the ๏ฌrst set of experiments, we simulate a single-round FFE problem with the Gowalla dataset [Cho et al., 2011]. ... In the second set of experiments, we run simulations on the Colossal Clean Crawled Corpus (C4) dataset [Bowman et al., 2020] ... In the third set of experiments, we run simulations on a Twitter dataset Sentiment-140 [Go et al., 2009]. |
| Dataset Splits | No | The paper describes how problem instances are constructed from datasets (e.g., sampling clients) and parameters for the sketching algorithm are set. However, it does not provide specific train/validation/test dataset splits in the traditional machine learning sense for model training and evaluation. |
| Hardware Specification | No | The paper mentions running 'simulations' and 'experiments' but does not specify any hardware details like GPU/CPU models, processors, or memory. |
| Software Dependencies | No | The paper describes the algorithms and their theoretical properties but does not specify any software dependencies with version numbers used for implementation or experimentation. |
| Experiment Setup | Yes | In the experiments, we ๏ฌx the con๏ฌdence parameter to be p = 0.1 and the sketch length to be L = ln(2d/p) โ 16. The targeted โโ-error ฯ is chosen evenly from (10โ3, 10โ1). ... We set the number of rounds to be M = 10. In each round, n = N/M = 17, 500 clients participate. |