Model-sharing Games: Analyzing Federated Learning Under Voluntary Participation
Authors: Kate Donahue, Jon Kleinberg5303-5311
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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, efficient 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. |