Federated Adversarial Domain Adaptation
Authors: Xingchao Peng, Zijun Huang, Yizhe Zhu, Kate Saenko
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we perform extensive experiments on several image and text classification tasks and show promising results under unsupervised federated domain adaptation setting. |
| Researcher Affiliation | Academia | Xingchao Peng Boston University Boston, MA 02215, USA xpeng@bu.edu Zijun Huang Columbia University New York City, NY 10027, USA zijun.huang@columbia.edu Yizhe Zhu Rutgers University Piscataway, NJ 08854, USA yz530@scarletmail.rutgers.edu Kate Saenko Boston University Boston, MA 02215, USA saenko@bu.edu |
| Pseudocode | Yes | Algorithm 1 Federated Adversarial Domain Adaptation |
| Open Source Code | No | The paper does not provide any explicit statements about open-sourcing code or links to a code repository. |
| Open Datasets | Yes | We test our model on the following tasks: digit classification (Digit-Five), object recognition (Office Caltech10 (Gong et al., 2012), Domain Net (Peng et al., 2018)) and sentiment analysis (Amazon Review dataset (Blitzer et al., 2007a)). |
| Dataset Splits | Yes | Table 10: Detailed number of samples we used in our experiments. Digit-Five Splits mnist mnist_m svhn syn usps Total Train 25,000 25,000 25,000 25,000 7,348 107,348 Test 9,000 9,000 9,000 9,000 1,860 37,860 [...] Domain Net Splits clp inf pnt qdr rel skt Total Train 34,019 37,087 52,867 120,750 122,563 49,115 416,401 Test 14,818 16,114 22,892 51,750 52,764 21,271 179,609 |
| Hardware Specification | Yes | We perform our experiments on a 10 Titan-Xp GPU cluster and simulate the federated system on a single machine (as the data communication is not the main focus of this paper). |
| Software Dependencies | No | Our model is implemented with Py Torch. However, no specific version number for PyTorch or any other software dependency is provided. |
| Experiment Setup | Yes | The detailed architecture of our model can be found in Table 7 (see supplementary material). ... Details of our model are listed in Table 9 (supplementary material). |