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..
FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction
Authors: Samiul Alam, Luyang Liu, Ming Yan, Mi Zhang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of Fed Rolex under two regimes: i) small-model small-dataset regime, and ii) large-model large-dataset regime |
| Researcher Affiliation | Collaboration | 1Michigan State University, 2The Ohio State University, 3Google Research, 4The Chinese University of Hong Kong, Shenzhen |
| Pseudocode | Yes | The pseudocode of Fed Rolex is in Algorithm 1. |
| Open Source Code | Yes | Our code is available at: https://github.com/AIo TMLSys-Lab/Fed Rolex. |
| Open Datasets | Yes | We train pre-activated Res Net18 (Pre Res Net18) models [26] on CIFAR-10 and CIFAR-100 [27]. ... For Stack Overflow, we followed [2] to use a 10% dropout rate to prevent overfitting, and 200 clients are randomly selected from a pool of 342, 477 clients in each communication round. The statistics of the datasets are listed in Table 2. |
| Dataset Splits | Yes | Table 2: Dataset statistics. ... Stack Overflow: Validation Clients 38,758, Validation Examples 16,491,230 |
| Hardware Specification | Yes | We implemented Fed Rolex and PT-based baselines using Py Torch [31] and Ray [32], and conducted our experiments on 8 NVIDIA A6000 GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch [31] and Ray [32]' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | The details of the hyper-parameters for model training are included in Appendix A.7. ... To ensure a fair comparison, all the PT-based baselines are trained using the same learning rate, number of communication rounds, and multi-step learning rate decay schedule. The details of the schedule for each dataset and experiment are described in Appendix A.7. |