  The increasing demand for intelligent services and privacy protection of
mobile and Internet of Things (IoT) devices motivates the wide application of
Federated Edge Learning (FEL), in which devices collaboratively train on-device
Machine Learning (ML) models without sharing their private data. Limited by
device hardware, diverse user behaviors and network infrastructure, the
algorithm design of FEL faces challenges related to resources, personalization
and network environments. Fortunately, Knowledge Distillation (KD) has been
leveraged as an important technique to tackle the above challenges in FEL. In
this paper, we investigate the works that KD applies to FEL, discuss the
limitations and open problems of existing KD-based FEL approaches, and provide
guidance for their real deployment.
