On Disentanglement of Asymmetrical Knowledge Transfer for Modality-Task Agnostic Federated Learning
Authors: Jiayi Chen, Aidong Zhang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate the superiority of our method on AFL than baselines. We evaluate Disent AFL on six AFL simulations, with at most 4 modalities and 4 downstream tasks. The empirical results demonstrate the effectiveness of our method. |
| Researcher Affiliation | Academia | Jiayi Chen, Aidong Zhang University of Virginia jc4td@virginia.edu, aidong@virginia.edu |
| Pseudocode | Yes | The training workflow and the pseudo-code of Disent AFL is provided in Algorithm 1 in the Supplementary Materials. |
| Open Source Code | No | The paper mentions 'Supplementary Materials' for pseudocode but does not explicitly state that the source code for the methodology is openly available or provide a link to a repository. |
| Open Datasets | Yes | AFL Simulation Setup We select seven multimodal or multitask datasets as the source to create six AFL simulations. The seven source datasets are summarized in Table 3 in Appendix, including two image classification datasets (Finn, Abbeel, and Levine 2017), a bimodal driving dataset (Duarte and Hu 2004), a bimodal 3D object recognition dataset (Wu et al. 2015; Feng et al. 2019), a three-modal two-task multimedia emotion recognition dataset and a bimodal audio-image classification dataset (Liang et al. 2021). |
| Dataset Splits | No | The paper mentions 'training' and 'testing accuracy' but does not explicitly provide details about the specific training, validation, and test dataset splits (e.g., percentages or sample counts) used for the experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments (e.g., specific GPU models, CPU models, or memory configurations). |
| Software Dependencies | No | The paper mentions 'PyTorch' as an implementation framework but does not specify its version or any other software dependencies with their version numbers. |
| Experiment Setup | Yes | The hyperparameters are listed in Appendix. |