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..
Heterogeneity for the Win: One-Shot Federated Clustering
Authors: Don Kurian Dennis, Tian Li, Virginia Smith
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We motivate our analysis with experiments on common FL benchmarks, and highlight the practical utility of one-shot clustering through usecases in personalized FL and device sampling. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University, Pittsburgh, PA, USA. Correspondence to: Don Dennis <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Local kpzq-means (Awasthi & Sheffet, 2012) and Algorithm 2 k-FED are provided with structured steps. |
| Open Source Code | Yes | Implementation of k-FED and experimental setup details can be found at: http://github.com/metastable B/kfed/. |
| Open Datasets | Yes | We perform this experiment on the FEMNIST and Shakespeare datasets (Caldas et al., 2018) (see Appendix B for details). ... We use the MNIST dataset for this experiment. |
| Dataset Splits | No | The paper mentions using datasets like FEMNIST, Shakespeare, and MNIST, but it does not specify explicit train/validation/test split percentages, sample counts, or reference predefined standard splits in detail for reproducibility. |
| Hardware Specification | No | The paper describes the context of federated learning involving 'mobile phones or wearables' but does not provide any specific hardware details such as GPU/CPU models, memory, or cloud computing specifications used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper states 'Implementation of k-FED and experimental setup details can be found at: http://github.com/metastable B/kfed/' (Section 4), indicating these details are external. The main text itself does not contain specific hyperparameters, optimizer settings, or other detailed system-level training configurations. |