Private Federated Frequency Estimation: Adapting to the Hardness of the Instance
Authors: Jingfeng Wu, Wennan Zhu, Peter Kairouz, Vladimir Braverman
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 uuujf@jhu.edu Wennan Zhu Google Research wennanzhu@google.com Peter Kairouz Google Research kairouz@google.com Vladimir Braverman Rice University vb21@rice.edu |
| 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 first 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 fix the confidence parameter to be p = 0.1 and the sketch length to be L = ln(2d/p) ≈ 16. The targeted ℓ∞-error τ is chosen evenly from (10−3, 10−1). ... We set the number of rounds to be M = 10. In each round, n = N/M = 17, 500 clients participate. |