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..
H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learning
Authors: He Yang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our H-FL framework achieves the state-of-art performance on different datasets for the real-world image recognition tasks. We evaluate H-FL on different datasets and compare the performance to Fed AVG [Mc Mahan et al., 2017a], STC [Sattler et al., 2019] and DGC [Lin et al., 2018] in non-IID environments. |
| Researcher Affiliation | Academia | Xi an Jiaotong University EMAIL |
| Pseudocode | Yes | Algorithm 1 Runtime distribution reconstruction; Algorithm 2 The workflow for H-FL |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | Yes | Specifically, we have trained a modified version of Le Net5 [Le Cun et al., 1998] network on FMNIST [Xiao et al., 2017] and a modified VGG16 [Simonyan and Zisserman, 2014] network network on CIFAR10 [Krizhevsky et al., 2009] respectively. |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits (percentages or sample counts). It only mentions using FMNIST and CIFAR10 in non-IID environments. |
| Hardware Specification | No | The paper does not mention any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The experiment settings are listed in Table 2. Dataset Clients Mediators η classes per client I L CIFAR10 100 3 0.015 3 10 1 FMNIST 100 3 0.015 2 10 1 |