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..
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. |