Federated edge learning for the wireless physical layer: Opportunities and challenges

Abstract

Deep learning (DL) has been applied to the physical layer of wireless communication systems, which directly extracts environment knowledge from data and outperforms conventional methods either in accuracy or computation complexity. However, most related research works employ centralized training that inevitably involves collecting training data from edge devices. The data uploading process usually results in excessive communication overhead and privacy disclosure. Alternatively, a distributed learning approach named federated edge learning (FEEL) is introduced to physical layer designs. In FEEL, all devices collaborate to train a global model only by exchanging parameters with a nearby access point. Because all datasets are kept local, data privacy is better protected and data transmission overhead can be reduced. This paper reviews the studies on applying FEEL to the wireless physical layer including channel state information acquisition, transmitter, and receiver design, which represent a paradigm shift of the DL-based physical layer design. In the meantime they also reveal several limitations inherent in FEEL, particularly when applied to the wireless physical layer, thus motivating further research efforts in the field.