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..
Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing
Authors: Mikhail Khodak, Renbo Tu, Tian Li, Liam Li, Maria-Florina F. Balcan, Virginia Smith, Ameet Talwalkar
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that Fed Ex can outperform natural baselines for federated hyperparameter tuning by several percentage points on the Shakespeare, FEMNIST, and CIFAR-10 benchmarks obtaining higher accuracy using the same training budget. |
| Researcher Affiliation | Collaboration | Mikhail Khodak, Renbo Tu, Tian Li Carnegie Mellon University EMAIL Liam Li Hewlett Packard Enterprise EMAIL Maria-Florina Balcan, Virginia Smith Carnegie Mellon University EMAIL,EMAIL Ameet Talwalkar Carnegie Mellon University & Hewlett Packard Enterprise EMAIL |
| Pseudocode | Yes | Algorithm 1: Successive halving algorithm (SHA) applied to personalized FL. Algorithm 2: Fed Ex |
| Open Source Code | Yes | 3. (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See link in the Appendix. |
| Open Datasets | Yes | evaluating on three standard FL benchmarks: Shakespeare, FEMNIST, and CIFAR-10 [5, 36]. |
| Dataset Splits | Yes | For Shakespeare and FEMNIST we use 80% of the data for training and 10% each for validation and testing. In CIFAR-10 we hold out 10K examples from the usual training/testing split for validation. |
| Hardware Specification | No | 3. (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [No] The main unit of cost in our setting is communication round, which we do report in e.g. Figure 4. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | No | The paper lists types of hyperparameters tuned (e.g., learning rate, batch-size, dropout) but defers the exact values or hyperparameter space to supplementary material: 'Please see the supplementary material for the exact hyperparameter space considered.' |