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..
SPACE: Single-round Participant Amalgamation for Contribution Evaluation in Federated Learning
Authors: Yi-Chung Chen, Hsi-Wen Chen, Shun-Gui Wang, Ming-syan Chen
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that SPACE outperforms state-of-the-art methods in terms of both running time and Pearson s Correlation Coefficient (PCC). Furthermore, extensive experiments conducted on applications, client reweighting, and client selection highlight the effectiveness of SPACE. |
| Researcher Affiliation | Academia | Yi-Chung Chen National Taiwan University EMAIL Hsi-Wen Chen National Taiwan University EMAIL Shun-Gui Wang National Taiwan University EMAIL Ming-Syan Chen National Taiwan University EMAIL |
| Pseudocode | Yes | Algorithm 1 SPACE |
| Open Source Code | Yes | The code is available at https://github.com/culiver/SPACE. |
| Open Datasets | Yes | we conduct experiments on the widely adopted image dataset MNIST [24] and CIFAR10 [22]. |
| Dataset Splits | No | The paper mentions using a 'validation set' multiple times (e.g., 'the evaluation of model performance depends on the size of the validation set on the server.', 'prototypes are built for the server s validation set.'), and uses standard datasets like MNIST and CIFAR10, but does not explicitly provide the specific percentages or counts for training, validation, and test splits. |
| Hardware Specification | Yes | Execution time (in seconds) is measured on a single V100 GPU without parallel training to assess the time efficiency of the approaches. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | For adjusting the utility function, we empirically set k as 100 while T as 0.95 and 0.5 for evaluation on MNIST and CIFAR10. |