Federated learning over wireless backhaul for distributed micro‐Doppler radars: Deep learning aided gradient estimation

Abstract

When micro‐Doppler (MD) radars are distributed, a federated learning strategy over wireless backhaul links is developed for motion classification. Specifically to identify the human motion, a common convolutional neural network (CNN) model is shared for all the distributed radars (i.e. clients) and it is trained through the federated learning strategy over wireless backhaul connected to the main server. In the proposed system, a main bottleneck is the estimation of local gradients for CNN training at the server, which are transferred from distributed radars over the wireless backhaul link. To overcome it, a deep learning (DL) aided gradient estimation algorithm is proposed, in which the deep neural networks (DNNs) for encoding local gradient vectors at the distributed radars and the DNN for decoding (i.e. estimating) them at the server are jointly trained in an end‐to‐end autoencoder‐based learning strategy. To avoid the inter‐client interference over the wireless backhaul link, the DNN structure for the gradient estimation algorithm with the orthogonal multiple access is proposed, in which the proposed DNN effectively learns the encoding/decoding at the transceiver over wireless backhaul. By exploiting the experimental data measured through the USRP‐based MD radars, the authors validate the motion classification performance of the proposed federated learning strategy and DL aided gradient estimation over the wireless backhaul link.

Federated Learning for Frequency-Modulated Continuous Wave Radar Gesture Recognition for Heterogeneous Clients

“…Within the relatively unexplored domain of using FL for radar sensors, Savazzi et al [11] investigated in 2021 a serverless FL approach, which addresses the task of tracking the position of individuals. In 2022, Yang et al proposed an autoencoder-based technique to encode local gradients from client NNs into a lower-dimensional latent representation to decrease the transmission error within a three-class classification task across three clients [12]. However, these current state-of-the-art methods of FL in the context of radar do not account for the effects of data sparsity, imbalanced data distributions, or varying levels of non-iid data.…”