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 Optimization with Doubly Regularized Drift Correction
Authors: Xiaowen Jiang, Anton Rodomanov, Sebastian U Stich
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate the main theoretical properties of our studied methods in numerical experiments on both simulated and real datasets.Figure 1. Illustrating communication reduction for DANE+-GD and Fed Red-GD on synthetic dataset using quadratic loss with L δA L δB 20. |
| Researcher Affiliation | Academia | 1CISPA Helmholtz Center for Information Security, Saarbr ucken, Germany 2Universit at des Saarlandes, Saarbr ucken, Germany. |
| Pseudocode | Yes | Algorithm 1 DANE+; Algorithm 2 Fed Red: Federated optimization framework with doubly Regularized drift correction; Algorithm 3 Fed Red-(S)GD |
| Open Source Code | No | The paper does not contain any explicit statements about open-sourcing code or links to a code repository for the described methodology. |
| Open Datasets | Yes | Binary classification on LIBSVM datasets. We experiment with the binary classification task on four real-world LIBSVM datasets (Chang and Lin, 2011). |
| Dataset Splits | No | The paper states 'We use n = 5 and split the dataset according to the Dirichlet distribution.' This describes the splitting methodology but does not specify exact percentages, counts, or cite predefined train/validation/test splits. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We set β = 0 for convex problems and β = 400 for the non-convex case. We further use n = 5, m = 10, and d = 1000. We use the constant probability ( 0.05) schedule for Fed Red GD. Lastly, we set the same step size for all three methods. We perform grid search to find the best hyper-parameters for each algorithm including the number of local steps and the stepsizes. |