A Bias Correction Mechanism for Distributed Asynchronous Optimization
Authors: Yuan Gao, Yuki Takezawa, Sebastian U Stich
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also conduct numerical experiments to corroborate our theoretical findings. ... 5 EXPERIMENTS In this section, we conduct numerical experiments to validate the theoretical results of our algorithms. |
| Researcher Affiliation | Academia | Yuan Gao EMAIL CISPA Helmholtz Center for Information Security Universität des Saarlandes Yuki Takezawa EMAIL Kyoto University OIST Sebastian Stich EMAIL CISPA Helmholtz Center for Information Security |
| Pseudocode | Yes | Algorithm 1 Async BC-GD ... Algorithm 2 Async BC-SGD |
| Open Source Code | Yes | All our code can be accessed at here and here. |
| Open Datasets | Yes | Regularized Logistic Regression For Fashion MNIST Classification Now we consider a regularized logistic regression problem for the opensourced Fashion MNIST dataset (Xiao et al., 2017). |
| Dataset Splits | Yes | We use 20% of the training dataset for the validation dataset. |
| Hardware Specification | Yes | All experiments were run on an Intel(R) Xeon(R) CPU E7-8890 v4 @ 2.20GHz chip. |
| Software Dependencies | No | The paper mentions 'All our code can be accessed at here and here.' but does not specify any software versions for frameworks or libraries used. |
| Experiment Setup | Yes | For the synthetic least squares problem, we set the number of clients n = 4, problem dimension d = 10, and target error (the average of the last 20 iterations) at 0.01. η is searched over {1.0 10 10, 5.0 10 10, , 1.0 10 1}. ... We set the number of clients n = 64, set the batch size to 32, and simulate the different computation speeds as in the previous section, where we set τ = 50. We perform a grid search over {0.5, 0.1, 0.05, 0.01, 0.005} for the best η parameter, and select the step size with the best average accuracy at the last 10 iterations. |