FLcode-lab/DADPFed-Code: DADPFed v1.0.0: Code for "Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition"
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2026
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| author | FLcode-lab |
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| contents | <p>This is the official code release accompanying the manuscript submitted to <em>The Visual Computer</em>.</p> <h2>Repository Overview</h2> <ul> <li><strong>Paper Title</strong>: Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition</li> <li><strong>Journal</strong>: The Visual Computer</li> <li><strong>Authors</strong>: Zhihao Liu, Wei Guo, Jie Wu, Mengke Zhu, Jiamin Liang</li> </ul> <h2>Contents</h2> <ul> <li>Full implementation of DADPFed framework</li> <li>Training/evaluation scripts for federated action recognition</li> <li>Subset construction, split files, and preprocessing pipelines for martial-arts video benchmarks</li> <li>Detailed README with environment setup and reproduction commands</li> </ul> <h2>Citation</h2> <p>If you use this code in your research, please cite our manuscript: Zhihao Liu, Wei Guo, Jie Wu, Mengke Zhu, Jiamin Liang. Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition. <em>The Visual Computer</em>, 2025.</p> <h2>Data Note</h2> <p>Raw video data is derived from public benchmarks (Kinetics-MA, UCF101-MA). Due to copyright constraints, raw videos are not included; all subset construction and preprocessing scripts are provided for full reproducibility.</p> |
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| id | zenodo_https___doi_org_10_5281_zenodo_19325591 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
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| spellingShingle | FLcode-lab/DADPFed-Code: DADPFed v1.0.0: Code for "Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition" FLcode-lab <p>This is the official code release accompanying the manuscript submitted to <em>The Visual Computer</em>.</p> <h2>Repository Overview</h2> <ul> <li><strong>Paper Title</strong>: Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition</li> <li><strong>Journal</strong>: The Visual Computer</li> <li><strong>Authors</strong>: Zhihao Liu, Wei Guo, Jie Wu, Mengke Zhu, Jiamin Liang</li> </ul> <h2>Contents</h2> <ul> <li>Full implementation of DADPFed framework</li> <li>Training/evaluation scripts for federated action recognition</li> <li>Subset construction, split files, and preprocessing pipelines for martial-arts video benchmarks</li> <li>Detailed README with environment setup and reproduction commands</li> </ul> <h2>Citation</h2> <p>If you use this code in your research, please cite our manuscript: Zhihao Liu, Wei Guo, Jie Wu, Mengke Zhu, Jiamin Liang. Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition. <em>The Visual Computer</em>, 2025.</p> <h2>Data Note</h2> <p>Raw video data is derived from public benchmarks (Kinetics-MA, UCF101-MA). Due to copyright constraints, raw videos are not included; all subset construction and preprocessing scripts are provided for full reproducibility.</p> |
| title | FLcode-lab/DADPFed-Code: DADPFed v1.0.0: Code for "Drift-Aware Dynamic Pruning: A Stabilising Heuristic for Heterogeneous Federated Action Recognition" |
| url | https://doi.org/10.5281/zenodo.19325591 |