Training Neural Networks on Remote Edge Devices for Unseen Class Classification

Conventionally, training a deep neural network (DNN) involves minimizing an empirical risk over a training dataset that comprises a certain number of classes. However, for training more versatile DNNs on edge devices, the training datasets are often updated to contain new classes that were not present in the original dataset. To this end, a naive approach could be to share the training samples corresponding to the newly-added classes with the edge devices. However, this comes at a huge communication cost. To tackle this issue, in this letter, we introduce a training method through which a parameter server (PS), having access to all the training samples including those for the newly-added classes, is able to train remote edge devices that lack access to the training samples for the new classes. To realize this, the PS sends an estimate of Bayes conditional probability distribution (BCPD) of the labels to the edge devices using which they train their local models. Via conducting some experiments, we demonstrate the effectiveness of the proposed method.