Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
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 | Input Tokens: 20,704 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,623 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 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. |