Optimality and Stability in Federated Learning: A Game-theoretic Approach
Authors: Kate Donahue, Jon Kleinberg
NeurIPS 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 | Kate Donahue Department of Computer Science Cornell University kdonahue@cs.cornell.edu Jon Kleinberg Departments of Computer Science and Information Science Cornell University kleinber@cs.cornell.edu |
| Pseudocode | No | The paper describes an algorithm in text (Theorem 1 and its explanation) but does not present it in a structured pseudocode or algorithm block format. |
| Open Source Code | Yes | The results in this paper are theoretical and do not depend on any experiments or code. However, while writing the paper, we found it useful to write and work with code to check conjectures. Some of that code is publicly available at github.com/kpdonahue/model_sharing_games. |
| Open Datasets | No | The results in this paper are theoretical and do not depend on any experiments or code, thus no datasets are used for training. |
| Dataset Splits | No | The results in this paper are theoretical and do not depend on any experiments or code, thus no dataset splits for validation are provided. |
| Hardware Specification | No | The results in this paper are theoretical and do not depend on any experiments or code, thus no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not rely on specific software dependencies with version numbers for its core findings. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup with hyperparameters or system-level training settings. |