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..
Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients
Authors: Aritra Mitra, Rayana Jaafar, George J. Pappas, Hamed Hassani
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide numerical results for Fed Lin on a least squares problem to validate our theory. In Appendix K, we also provide additional numerical results on a logistic regression problem. |
| Researcher Affiliation | Academia | Department of Electrical and Systems Engineering EMAIL |
| Pseudocode | Yes | Algorithm 1 Fed Lin |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | No | To generate synthetic data, for each client i S = {1, . . . , 20}, we generate Ai and bi according to the model bi = Aixi + εi, where xi is a weight vector and εi R500 is a disturbance. In particular, we generate [Ai]jk i.i.d. N(0, 1), and εi N(0, 0.5I500), i S. |
| Dataset Splits | No | The paper describes generating synthetic data and states it will focus on a deterministic setting for experiments, but it does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | 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] |
| Software Dependencies | No | The paper describes specific hyperparameters (e.g., step size η), but it does not provide details on specific software dependencies or their version numbers (e.g., libraries, frameworks, or solvers). |
| Experiment Setup | Yes | The constant η is fixed at 10^−2. ... The constant η is fixed at 5 × 10^−4. ... We set the number of local steps H = 20, the statistical heterogeneity parameter α = 10, and use a step-size of 10^−3 for both algorithms (the step-size was tuned to get best results). |