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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Correlated Quantization for Distributed Mean Estimation and Optimization
Authors: Ananda Theertha Suresh, Ziteng Sun, Jae Ro, Felix Yu
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed algorithm outperforms existing mean estimation protocols on a diverse set of tasks. We demonstrate that the proposed algorithm outperforms existing baselines on several distributed tasks. |
| Researcher Affiliation | Industry | 1Google Research, New York. |
| Pseudocode | Yes | Algorithm 1 ONEDIMONEBITCQ Input: x1, x2, . . . , xn, l, r. Generate π, a random permutation of {0, 1, 2, . . . , n 1}. |
| Open Source Code | Yes | We implement all algorithms and experiments using the open-source JAX (Bradbury et al., 2018) and Fed JAX (Ro et al., 2021) libraries 3. 3https://github.com/google-research/google-research/tree/master/correlated_compression |
| Open Datasets | Yes | We also compare quantizers on the distributed mean estimation task for the MNIST (d = 784) dataset distributed over 100 clients. We use the image recognition task for the Federated MNIST dataset (Caldas et al., 2018a) provided by Tensor Flow Federated (Bonawitz et al., 2019). |
| Dataset Splits | No | The paper does not explicitly provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | We implement all algorithms and experiments using the open-source JAX (Bradbury et al., 2018) and Fed JAX (Ro et al., 2021) libraries |
| Experiment Setup | Yes | We first fix the number of clients n to be 100, k = 2, and vary σmd. We then fix σmd = 0.01, n = 100 and vary k. Finally, we fix σmd = 0.01, k = 2 and vary n. The experiments are averaged over ten runs for statistical consistency. We use 2-level quantization (one bit) for all the algorithms, except Tern Grad which uses 3 levels and hence requires log2(3) bits per coordinate per client. |