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..
Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
Authors: Kate Donahue, Jon Kleinberg5303-5311
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The results in this paper are theoretical and do not depend on any experiments or code. |
| Researcher Affiliation | Academia | 1 Department of Computer Science, Cornell University 2 Department of Information Science, Cornell University |
| Pseudocode | No | The paper describes algorithms (e.g., 'produce a simple, ef๏ฌcient algorithm to calculate a strictly core-stable arrangement') but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Some of that code is publicly available at github.com/kpdonahue/model_sharing_games. |
| Open Datasets | No | The paper is theoretical and does not involve experiments or datasets for training models. Therefore, it does not specify any publicly available datasets used for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experiments or datasets. Therefore, it does not provide training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and states that its results do not depend on any experiments. Therefore, it does not specify any hardware used for running experiments. |
| Software Dependencies | No | The paper mentions that some code is available on GitHub but does not list any specific software dependencies with version numbers needed for reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not involve experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or training configurations. |