Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation
Authors: Berivan Isik, Wei-Ning Chen, Ayfer Ozgur, Tsachy Weissman, Albert No
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate the communication-privacy-utility tradeoffs of RRSC and compare it with order-optimal schemes under privacy and communication constraints, namely SQKR [6] and MMRC [30]. |
| Researcher Affiliation | Academia | Berivan Isik Stanford University berivan.isik@stanford.edu Wei-Ning Chen Stanford University wnchen@stanford.edu Ayfer Ozgur Stanford University aozgur@stanford.edu Tsachy Weissman Stanford University tsachy@stanford.edu Albert No Hongik University albertno@hongik.ac.kr |
| Pseudocode | Yes | We call this approach Randomly Rotating Simplex Coding (RRSC) and provide the pseudocode in Algorithm 1. |
| Open Source Code | Yes | The codebase for this work is open-sourced at https://github.com/Berivan Isik/rrsc. |
| Open Datasets | No | The paper uses synthetically generated data: 'More precisely, for the first half of the users, we set v1, . . . , vn/2 i.i.d N(1, 1) d; and for the second half of the users, we set vn/2+1, . . . , vn i.i.d N(10, 1) d. We further normalize each vi to ensure that they lie on Sd 1.' It does not provide access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes the generation of synthetic data but does not specify train/validation/test splits or cross-validation for experimental reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | To find the optimal values for k and rk, we compute the optimal rk using the formula in (33) for k = 1, . . . , M and pick the k that gives the smallest rk (which corresponds to the bias). To estimate the expectation Ck in (33), we run a Monte Carlo simulation with 1M trials. |