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].
Learning to Prompt Your Domain for Federated Vision-Language Models
Authors: Guoyizhe Wei, Feng Wang, Anshul Shah, Rama Chellappa
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate ADAPT s significant efficiency and effectiveness in federated learning. For example, by learning and sharing only 0.35M parameters, ADAPT attains a 69.8% average accuracy over the six domains of Domain Net, which improves the original CLIP accuracy by 16.2%. |
| Researcher Affiliation | Academia | Guoyizhe Wei Johns Hopkins University Feng Wang Johns Hopkins University Anshul Shah Johns Hopkins University Rama Chellappa Johns Hopkins University |
| Pseudocode | No | The paper includes 'Figure 1: Local training framework' which illustrates the process, but it is a diagram with descriptive text rather than a formal pseudocode block or algorithm. |
| Open Source Code | No | The paper does not contain an unambiguous sentence where the authors state they are releasing the code for the work described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We evaluate the proposed ADAPT and baseline methods on three domain adaptation image classification benchmarks: the Domain Net (Peng et al., 2019), Office Home (Venkateswara et al., 2017), and PACS (Li et al., 2017) datasets, presented in the Appendix. [...] both pretrained on Image Net-1k (Deng et al., 2009). |
| Dataset Splits | Yes | By default, the number of clients is determined by the number of domains for each dataset, i.e. n = 6 for Domain Net and n = 4 for Office Home and PACS. [...] In our ablation study, we also further divide each domain into five splits with non-i.i.d. categories. |
| Hardware Specification | No | The paper does not mention specific hardware details like GPU models (e.g., NVIDIA A100), CPU models (e.g., Intel Xeon), or other processor types used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like SGD and AdamW but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x, CUDA x.x). |
| Experiment Setup | Yes | We train both our model and the baseline models for 200 epochs and execute the aggregation or broadcast process after every one epoch. We train the Res Net-based models and prompt tokens by a SGD optimizer with 0.01 learning rate, 0.9 momentum, and 0.005 weight decay. ADAPT instead uses Adam W (Loshchilov & Hutter, 2019) optimizer with β1 = 0.9, β2 = 0.999, 5e-4 learning rate, and 0.01 weight decay for transformer-based models. We set the temperature coefficient τd = 0.1 in Equation 4, and set the momentum update ratio α = 0.99 in Equation 6. If not specified, all reported results are average numbers over three trials. |