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 [1].
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. |