Abstract
Transfer learning, which transfers knowledge from
source datasets to target datasets, is practical for adaptive deep
neural network (DNN) applications. When considering user pri-
vacy and communication bandwidth issues, edge devices’ training
is essential for transfer learning. Nevertheless, training requires
repeating feedforward (FF), backpropagation (BP), and weight
gradient (WG) millions of times, introducing prohibitive compu-
tation for edge devices. A promising method to reduce training
c