minibatch-sgd
Country:
- Asia > Middle East > Israel > Haifa District > Haifa (0.04)
- North America > United States > Virginia (0.04)
Technology:
Country:
- Asia > Middle East > Israel > Haifa District > Haifa (0.04)
- North America > United States > Virginia (0.04)
Technology:
SLowcal-SGD: Slow Query Points Improve Local-SGD for Stochastic Convex Optimization
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.
2304.04169
Technology: