LAUSR

Federated learning (FL) deployed in the edge network environment is a promising approach for combining the separated training results based on the isolated local data sensed by various Internet of Things (IoT) devices. However, the limited computing resources for the training of various application models in each edge server and the communication burden among the edge server and numerous IoT devices greatly impact the realization of IoT intelligence. In this article, we propose transform-domain FL schemes based on discrete cosine transform (DCT-FA) and discrete wavelet transform (DWT-FA) to achieve better training efficiency and reduce the communication burden for IoT devices. Furthermore, when the amount of training data is limited, we propose to combine time-domain features and frequency-domain features in FL (CDCT-FA) that turns out to achieve much higher test accuracy. From the experimental results, the transform-domain FL schemes are shown to be promising, given the different constraints and requirements of various IoT intelligence applications.