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 [1].
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Authors: Rui Hu, Yanmin Gong, Yuanxiong Guo
IJCAI 2021 | Venue PDF | LLM Run Details
| 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. |