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.
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Authors: Rui Hu, Yanmin Gong, Yuanxiong Guo
IJCAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,566 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,397 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 | Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency. |
| Researcher Affiliation | Academia | Rui Hu , Yanmin Gong and Yuanxiong Guo The University of Texas at San Antonio EMAIL |
| Pseudocode | Yes | Algorithm 1 The Fed-SPA Algorithm |
| Open Source Code | No | The paper provides a link to its full version on arXiv (https://arxiv.org/abs/2008.01558) but does not state that source code for the described methodology is publicly available. |
| Open Datasets | Yes | We explore two widely-used benchmark datasets in FL: MNIST [Le Cun et al., 1998] and CIFAR-10 [Krizhevsky et al., 2009]. |
| Dataset Splits | No | The paper mentions training and testing examples but does not explicitly describe a validation dataset split (e.g., specific percentages or counts for a validation set). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set the number of local iterations τ = 300 for MNIST and τ = 50 for CIFAR-10. The details of other hyperparameter settings are given in the full version. |