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].
FedSR: A Simple and Effective Domain Generalization Method for Federated Learning
Authors: A. Tuan Nguyen, Philip Torr, Ser Nam Lim
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results suggest that our method significantly outperforms relevant baselines in this particular problem. |
| Researcher Affiliation | Collaboration | A. Tuan Nguyen University of Oxford EMAIL Philip H. S. Torr University of Oxford EMAIL Ser-Nam Lim Meta AI Research EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code will be released at https://github.com/atuannguyen/Fed SR. |
| Open Datasets | Yes | To evaluate our method, we perform experiments in four datasets (ranging from easy to more challenging) that are commonly used in the literature for domain generalization. Rotated MNIST [10], PACS [20], Office Home [40], Domain Net [33]. |
| Dataset Splits | Yes | Following standard practice, we use 90% of available data as training data and 10% as validation data. |
| Hardware Specification | Yes | We train all our models with NVIDIA A100 GPUs from our AWS cluster. |
| Software Dependencies | No | The paper mentions software components and models like Resnet18, ResNet50, SGD, and ImageNet, but does not provide specific version numbers for any software libraries or frameworks used. |
| Experiment Setup | Yes | For the Rotated MNIST dataset... train our network for 500 epochs with stochastic gradient descent (SGD), using a learning rate of 0.001 and minibatch size 64, and report performance on the test domain after the last epoch. Each client performs 5 local optimization iterations within each communication round (E = 5). For the PACS datasets... Each local client uses stochastic gradient descent (SGD) (a total of 5000 iterations) with learning rate 0.01, momentum 0.9, minibatch size 64, and weight decay 5e 4. |