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 Composite Optimization
Authors: Honglin Yuan, Manzil Zaheer, Sashank Reddi
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical analysis and empirical experiments demonstrate that FEDDUALAVG outperforms the other baselines. |
| Researcher Affiliation | Collaboration | 1Stanford University 2Based on work performed at Google Research 3Google Research. Correspondence to: Honglin Yuan <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Federated Averaging (FEDAVG) ... Algorithm 2 Federated Mirror Descent (FEDMID) ... Algorithm 3 Federated Dual Averaging (FEDDUALAVG) |
| Open Source Code | Yes | The source code is available at https://github.com/hongliny/FCO-ICML21. |
| Open Datasets | Yes | For the purpose of illustration, in Fig. 1, we present results on a federated sparse ( 1-regularized) logistic regression task for an f MRI dataset based on (Haxby, 2001). |
| Dataset Splits | Yes | We select five (out of six) subjects as the training set and the last subject as the held-out validation set. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper mentions using 'the nilearn package (Abraham et al., 2014)' but does not provide specific version numbers for this or any other software dependency, which is required for reproducibility. |
| Experiment Setup | Yes | The best learning rates configuration is c = 0.01, s = 1 for FEDDUALAVG, and c = 0.001, s = 0.3 for other algorithms (including FEDMID). ... We set the 1-regularization strength to be 10 3. For each setup, we run the federated algorithms for 300 communication rounds. |