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..
Hierarchical Federated Learning with Multi-Timescale Gradient Correction
Authors: Wenzhi Fang, Dong-Jun Han, Evan Chen, Shiqiang Wang, Christopher Brinton
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on various datasets and models, we validate the effectiveness of MTGC in diverse HFL settings. |
| Researcher Affiliation | Collaboration | Wenzhi Fang Purdue University EMAIL Dong-Jun Han Yonsei University EMAIL Evan Chen Purdue University EMAIL Shiqiang Wang IBM Research EMAIL Christopher G. Brinton Purdue University EMAIL |
| Pseudocode | Yes | Algorithm 1: HFL with Multi-Timescale Gradient Correction (MTGC) |
| Open Source Code | Yes | The code for this project is available at https://github.com/wenzhifang/MTGC. |
| Open Datasets | Yes | In our experiments, we consider four widely used datasets: EMNIST-Letters (EMNIST-L) [7], Fashion-MNIST [53], CIFAR-10 [23], and CIFAR-100 [23]. |
| Dataset Splits | Yes | The CINIC-10 dataset contains 90,000 training images, 90,000 validation images, and 90,000 test images, significantly larger than CIFAR-10 and CIFAR-100 with 60,000 images. |
| Hardware Specification | Yes | We conduct the experiments based on a cluster of 3 NVIDIA A100 GPUs with 40 GB memory. |
| Software Dependencies | No | The paper mentions "Our code is based on the framework of [1]" but does not specify particular software dependencies with version numbers (e.g., Python version, specific library versions like PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | Across all algorithms considered, we maintain a consistent learning rate η = 0.1 and batch size 50. |