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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SpaFL: Communication-Efficient Federated Learning With Sparse Models And Low Computational Overhead
Authors: Minsu Kim, Walid Saad, Merouane DEBBAH, Choong Hong
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Spa FL improves accuracy while requiring much less communication and computing resources compared to sparse baselines. |
| Researcher Affiliation | Academia | Minsu Kim Virginia Tech Walid Saad Virginia Tech Merouane Debbah Khalifa University Choong Seon Hong Kyung Hee University |
| Pseudocode | Yes | The overall algorithm is illustrated in Fig 1. and summarized in Algorithm 1. |
| Open Source Code | Yes | The code is available at https://github.com/news-vt/Spa FL_Neru IPS_2024 |
| Open Datasets | Yes | We conduct experiments on three image classification datasets: FMNIST [37], CIFAR-10, and CIFAR-100 [38] datasets with NVIDA A100 GPUs. To distribute datasets in a non-iid fashion, we use Dirichlet (0.2) for FMNIST and Dirichlet (0.1) for CIFAR-10 and CIFAR-100 datasets as done in [39] with N = 100 clients. |
| Dataset Splits | No | The paper specifies training data distribution and explicitly mentions a 'test dataset' for evaluation, but it does not specify a separate 'validation' dataset split. |
| Hardware Specification | Yes | We conduct experiments on three image classification datasets: FMNIST [37], CIFAR-10, and CIFAR-100 [38] datasets with NVIDA A100 GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch' in the appendix, but it does not specify a version number for PyTorch or any other software library. |
| Experiment Setup | Yes | For FMNIST dataset, we use the Lenet-5-Caffe. For the Lenet model, we set η(t) = 0.001, E = 5, α = 0.002, and a batch size to be 64. For CIFAR-10 dataset, we use a convolutional neural network (CNN) model with seven layers used in [40] with η(t) = 0.01, E = 5, α = 0.00015, and a batch size of 16. We adopt the Res Net-18 model for CIFAR-100 dataset with η(t) = 0.01, E = 7, α = 0.0007, and a batch size of 64. The learning rate of CIFAR-100 is decayed by 0.993 at each communication round. |