تقرير
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
| العنوان: | SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization |
|---|---|
| المؤلفون: | Levy, Kfir Y. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Mathematics - Optimization and Control |
| الوصف: | We consider distributed learning scenarios where M machines interact with a parameter server along several communication rounds in order to minimize a joint objective function. Focusing on the heterogeneous case, where different machines may draw samples from different data-distributions, we design the first local update method that provably benefits over the two most prominent distributed baselines: namely Minibatch-SGD and Local-SGD. Key to our approach is a slow querying technique that we customize to the distributed setting, which in turn enables a better mitigation of the bias caused by local updates. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.04169 |
| رقم الأكسشن: | edsarx.2304.04169 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |