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].
Principled Federated Domain Adaptation: Gradient Projection and Auto-Weighting
Authors: Enyi Jiang, Yibo Jacky Zhang, Sanmi Koyejo
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments verify the theoretical insights and illustrate the effectiveness of the proposed methods in practice. |
| Researcher Affiliation | Academia | Enyi Jiang EMAIL UIUC Yibo Jacky Zhang EMAIL Stanford Sanmi Koyejo EMAIL Stanford |
| Pseudocode | Yes | Algorithm 1 FDA: Gradient Projection and the Auto-Weighting Scheme |
| Open Source Code | Yes | Our code is at https://github.com/jackyzyb/Auto Fed GP. |
| Open Datasets | Yes | Colored MNIST (Arjovsky et al., 2019), VLCS (Fang et al., 2013), Terra Incognita (Beery et al., 2018) datasets... Fashion-MNIST (Xiao et al., 2017) and CIFAR-10 (Krizhevsky et al., 2009) datasets |
| Dataset Splits | Yes | For source domains, we split the training/testing data with a 20% and 80% split. |
| Hardware Specification | Yes | In a real implementation, the whole process of projection is fast, with around 0.023 seconds per call needed for N = 10 clients of Fashion-MNIST experiments on the NVIDIA TITAN Xp hardware with GPU available. |
| Software Dependencies | No | The paper mentions using Adam (Kingma & Ba, 2014) optimizer, CNN models, and ResNet-18 (He et al., 2016), but it does not specify version numbers for any software libraries or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | We set the communication round R = 50 and the local update epoch to 1, with 10 clients (1 target, 9 source clients) in the system... For the experiments on the Fashion-MNIST dataset, we set the source learning rate to be 0.01 and the target learning rate to 0.05. For CIFAR-10, we use a 0.005 source learning rate and a 0.0025 learning rate. The source batch size is set to 64 and the target batch size is 16. |