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..
On Large-Cohort Training for Federated Learning
Authors: Zachary Charles, Zachary Garrett, Zhouyuan Huo, Sergei Shmulyian, Virginia Smith
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give partial answers to these questions based on extensive empirical evaluation. |
| Researcher Affiliation | Collaboration | Zachary Charles Google EMAIL Zachary Garrett Google EMAIL Zhouyuan Huo Google EMAIL Sergei Shmulyian Google EMAIL Virginia Smith Carnegie Mellon University EMAIL |
| Pseudocode | Yes | Algorithm 1 Fed Opt framework |
| Open Source Code | Yes | We provide open-source implementations of all simulations in Tensor Flow Federated [4]2. 2https://github.com/google-research/federated/tree/f4e26c1b9b47ac320e520a8b9943ea2c5324b8c2/ large_cohort |
| Open Datasets | Yes | We use four datasets: CIFAR-100 [35], EMNIST [13], Shakespeare [8], and Stack Overflow [3]. |
| Dataset Splits | Yes | We tune learning rates for all algorithms and models using a held-out validation set: We perform T = 1500 rounds of training with M = 50, E = 1 for each algorithm and model, varying ηc, ηs over {10i | 3 i 1} and select the values that maximize the average validation performance over 5 random trials. |
| Hardware Specification | No | All experiments were conducted using clusters of multi-core CPUs, though our results are independent of wall-clock time and amount of compute resources. (This is too general; no specific CPU models or detailed cluster specs are provided). |
| Software Dependencies | No | We provide open-source implementations of all simulations in Tensor Flow Federated [4]2. (A version number for TensorFlow Federated or other libraries is not specified). |
| Experiment Setup | Yes | We set pk to be the number of examples in client k s dataset. We tune learning rates for all algorithms and models using a held-out validation set: We perform T = 1500 rounds of training with M = 50, E = 1 for each algorithm and model, varying ηc, ηs over {10i | 3 i 1} and select the values that maximize the average validation performance over 5 random trials. All other hyperparameters (such as momentum) are fixed. |