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..
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Authors: Pei-Yau Weng, Minh Hoang, Lam Nguyen, My T. Thai, Lily Weng, Nghia Hoang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our reported results on a variety of computer vision datasets confirm that the proposed method is most effective to combat extreme data heterogeneity in federated learning. |
| Researcher Affiliation | Collaboration | Pei-Yau Weng Washington State University EMAIL Minh Hoang Princeton University EMAIL Lam M. Nguyen IBM Research EMAIL My T. Thai University of Florida EMAIL Tsui-Wei Weng University of California San Diego EMAIL Trong Nghia Hoang Washington State University EMAIL |
| Pseudocode | Yes | Algorithm 1 Probabilistic Federated Prompt Tuning (PFPT) input: pre-trained model F, no. τ of iterations, no. m of sampled clients per iteration output: optimized set of prompts Φ |
| Open Source Code | Yes | Code Release. Our experimental code is released and maintained at https://github.com/Pei Yau Weng/PFPT. |
| Open Datasets | Yes | Our experiments are conducted on a variety of computer vision datasets, including CIFAR10 and CIFAR-100 [51], Tiny Image Net [52] and a synthetic, diverse dataset created by pooling together the MNIST-M [53], Fashion-MNIST [54], CINIC-10 [55] and MMAFEDB (available on Kaggle) datasets, which is referred to as the 4-dataset. ... All the used datasets are publicly available. |
| Dataset Splits | No | The paper mentions 'train partition' and 'test partition' but does not explicitly state a separate 'validation' split for model tuning or early stopping. |
| Hardware Specification | Yes | All experiments are performed on a V100 GPU with 32GB GPU RAM. |
| Software Dependencies | No | The paper mentions optimizers like Adam and SGD but does not provide specific version numbers for software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | The hyperparameter settings are all presented in Table 9. Table 9: Hyperparameter setting for all baselines and our PFPT Method Setting Batch size Communication round Eps. in local training Optimizer & learning rate Total clients Sampled clients |