Model Aggregation Method for Data Parallelism in Distributed Real-Time Machine Learning of Smart Sensing Equipment

In distributed real-time machine learning of smart sensing equipment, training speed and training accuracy are two hard-to-choose trade-off performance measures directly influenced by the design of distributed machine learning algorithms. And it will influence effort of smart sensing equipment directly. We take the model aggregation method of distributed machine learning as a starting point. Due to the loss of accuracy caused by the direct averaging of the parameter average method, we developed the loss function weight reorder stochastic gradient descent method (LR-SGD). LR-SGD uses the loss function value to determine the weight of the work nodes when aggregating the model parameters, and it improves the performance of the parameter average method for nonconvex problems. As shown in the experiment results, our algorithm can improve the training accuracy by a maximum of approximately 0.57% for the Bulk Synchronous Parallel (BSP) model and approximately 6.30% for the Stale Synchronous Parallel (SSP) model.