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].
QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning
Authors: Liping Yi, Wang Gang, Liu Xiaoguang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experiments We implement QSFL3 on the FL framework developed in Luo et al. (2019) and use 4 NVIDIA Ge Force RTX 3090 GPUs to execute QSFL parallelly. We evaluate QSFL in an image classification task and an object detection task. |
| Researcher Affiliation | Collaboration | 1Nankai-Orange D.T. Joint Lab, College of Computer Science, Nankai University, Tianjin, China. Correspondence to: Gang Wang <EMAIL>, Xiaoguang Liu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 SCSS Algorithm |
| Open Source Code | Yes | QSFL3 on the FL framework developed in Luo et al. (2019)... 3https://github.com/LipingYi/QSFL |
| Open Datasets | Yes | CNN on FEMINIST: we train a CNN network (2Conv + 1FC) with 110526 parameters on a real-world FEMINIST4 dataset (Caldas et al., 2018b). ... 4https://github.com/TalwalkarLab/leaf/tree/master/data/FEMINIST |
| Dataset Splits | No | Each client s local dataset is divided into training/testing sets with a ratio of 8:2. ... No explicit mention of a validation split for either dataset. |
| Hardware Specification | Yes | use 4 NVIDIA Ge Force RTX 3090 GPUs to execute QSFL parallelly. |
| Software Dependencies | No | The paper mentions implementing QSFL on an FL framework and provides a GitHub link, but it does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | We also report detailed hyperparameters settings of FL in the two tasks, as shown in Tab. 7. Table 7: Hyperparameters settings of FL. C: total number of clients, η: learning rate; E: epoch, B: batch size. CNN on FEMINIST C 36 η 0.01 E 10 B 1 |