During the training of lightweight DNN, we introduce a novel early halting technique, which preserves network resources; thus, speedups the training procedure.
With the enhancement of people's living standards and rapid growth of communication technologies, residential environments are becoming smart and well-connected, increasing overall energy consumption substantially.
In this paper, we propose a federated learning approach to suppress the unequal distribution of the noisy labels in the dataset of each participant.
In this paper, we present a comprehensive review of existing literature on compressing DNN model that reduces both storage and computation requirements.
As most of the approaches have solved the early classification problem with different aspects, it becomes very important to make a thorough review of the existing solutions to know the current status of the area.