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..
Towards Understanding Ensemble Distillation in Federated Learning
Authors: Sejun Park, Kihun Hong, Ganguk Hwang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide experimental results to verify our theoretical results on ensemble distillation in federated learning. |
| Researcher Affiliation | Academia | 1Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea. |
| Pseudocode | Yes | Algorithm 1 KRR with iterative Ensemble Distillation in FL |
| Open Source Code | No | No explicit statement providing concrete access to source code for the methodology described in this paper was found. No repository links or explicit code release statements were present. |
| Open Datasets | Yes | The real world dataset is a simplified regression version of the MNIST dataset from another existing work (Cui et al., 2021). |
| Dataset Splits | No | The paper mentions training data and test data, but no explicit details about a separate validation split (percentages, counts, or methodology) are provided. It states: 'In the test phase, we use a test dataset of size 1000 whose data points are generated from the procedure explained in Appendix E.1.' |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or relevant libraries with their versions) were listed in the paper. |
| Experiment Setup | Yes | We conduct experiments with the local datasets of sizes N = 10 and N = 20. ... we set the unlabeled public dataset size Np = (m 1)N. For the iterative ensemble distillation algorithm, set the total communication round t = 200 for convergence. We use the fixed distillation hyperparameter α = 1/m but conduct the hyperparemeter tuning for λ > 0 using the grid search. |