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 [1].
FedMBridge: Bridgeable Multimodal Federated Learning
Authors: Jiayi Chen, Aidong Zhang
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on four AMFL simulations demonstrate the efficiency and effectiveness of our proposed approach. and 5. Experiments, Table 2 reports the results on all simulations, comparing Fed MBridge with baseline approaches. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA. |
| Pseudocode | Yes | Algorithm 1 Fed MBridge in Appendix B.2. |
| Open Source Code | No | We will release our AMFL simulations in the future. This statement refers to the datasets, not the source code for the proposed methodology. There is no explicit statement or link indicating that the source code for Fed MBridge is released or available. |
| Open Datasets | Yes | Scene AMF is constructed from the bimodal NYU-v2 dataset (Nathan Silberman & Fergus, 2012) Object AMF is constructed from the bimodal Model Net40 dataset (Wu et al., 2015) Emotion AMF is created from the CMU-MOSEI dataset (Liang et al., 2021) Mnist AMF is made from AVMnist (Liang et al., 2021) and Multi Mnist (Sabour et al., 2017) datasets |
| Dataset Splits | No | The paper mentions training samples and testing samples for the datasets, but does not explicitly specify the use of a validation set or provide details on how such a split would be generated or used for model selection/hyperparameter tuning. For example: Each client i have ni Normal(500, 100) training samples and 1,000 testing samples from the selected 5 classes. |
| Hardware Specification | Yes | All the approaches are implemented using Py Torch 3.7 and we ran all experiments on a single A800. |
| Software Dependencies | Yes | All the approaches are implemented using Py Torch 3.7 |
| Experiment Setup | Yes | B.3. Hyperparameters The hyperparameters are listed in Table 5. Table 5. List of Hyperparameters. Where Notations/Descriptions Values local optimizers Adam local learning rate (α) 0.1 0.02 # training epoch per round 15 training batch size 128 client selection ratio (|Nr|/N) 0.25 # GNN layers (L) 4 node feature size per GNN layer [32, 64, 64, 32] layer-role embedding size (S) 32 task embedding size (F) 16 learning rate of TAHN (η) 0.07 0.005 optimizer of TAHN SGD weight decay 0.06 |