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..
Convergence Analysis of Sequential Federated Learning on Heterogeneous Data
Authors: Yipeng Li, Xinchen Lyu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results validate the counterintuitive analysis result that SFL outperforms PFL on extremely heterogeneous data in cross-device settings. |
| Researcher Affiliation | Academia | Yipeng Li and Xinchen Lyu National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing, 100876, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Sequential FL; Algorithm 2: Parallel FL |
| Open Source Code | Yes | Our code is partly from Gao et al. (2021); Zeng et al. (2021); Jhunjhunwala et al. (2023) (more references are included in the code), and it is available at https://github.com/liyipeng00/convergence. |
| Open Datasets | Yes | We partition the training sets of CIFAR-10 (Krizhevsky et al., 2009) and CINIC-10 (Darlow et al., 2018). |
| Dataset Splits | No | The paper describes partitioning 'training sets' and sparing 'test sets' but does not explicitly mention or detail a validation set or its split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions using VGGs and Res Nets models, and SGD as a local solver, but does not provide specific version numbers for software frameworks, libraries, or dependencies. |
| Experiment Setup | Yes | We fix the number of participating clients to 10 and the mini-batch size to 20. The local solver is SGD with learning rate being constant, momentem being 0 and weight decay being 1e-4. We apply gradient clipping to both algorithms (Appendix G.2) and tune the learning rate by grid search (Appendix G.3). |