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..
Language-Guided Transformer for Federated Multi-Label Classification
Authors: I-Jieh Liu, Ci-Siang Lin, Fu-En Yang, Yu-Chiang Frank Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments on various multi-label datasets (e.g., FLAIR, MSCOCO, etc.), we show that our Fed LGT is able to achieve satisfactory performance and outperforms standard FL techniques under multi-label FL scenarios. Code is available at https://github.com/Jack24658735/Fed LGT. |
| Researcher Affiliation | Collaboration | I-Jieh Liu1, Ci-Siang Lin1,2, Fu-En Yang1,2, Yu-Chiang Frank Wang1,2 1Graduate Institute of Communication Engineering, National Taiwan University 2NVIDIA |
| Pseudocode | Yes | The pseudo-code of our proposed framework is described in Algorithm 1. |
| Open Source Code | Yes | Code is available at https://github.com/Jack24658735/Fed LGT. |
| Open Datasets | Yes | we conduct extensive evaluations on various benchmark datasets, including FLAIR (Song, Granqvist, and Talwar 2022), MS-COCO (Lin et al. 2014), and PASCAL VOC (Everingham et al. 2015). |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, and test splits for the datasets. It mentions 'FLAIR provides real-user data partitions' but no details on the splits. |
| Hardware Specification | Yes | For all experiments, we implement our model by Py Torch and conduct training on a single NVIDIA RTX 3090Ti GPU with 24GB memory. |
| Software Dependencies | No | The paper mentions 'Py Torch' as the implementation framework and 'Adam' as the optimizer, but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | set the threshold τ to 0.5 and the uncertainty margin ε is 0.02. For each round of local training, we train 5 epochs using the Adam (Kingma and Ba 2014) optimizer with a learning rate of 0.0001, and the batch size is set to 16. For the detail settings about FL, the communication round T is set to 50, and the fraction of active clients in each round is designed to achieve a level of participation equivalent to 50 clients, thus ensuring the data distribution is representative of the overall population. |