  Wider coverage and a better solution to a latency reduction in 5G necessitate
its combination with multi-access edge computing (MEC) technology.
Decentralized deep learning (DDL) such as federated learning and swarm learning
as a promising solution to privacy-preserving data processing for millions of
smart edge devices, leverages distributed computing of multi-layer neural
networks within the networking of local clients, whereas, without disclosing
the original local training data. Notably, in industries such as finance and
healthcare where sensitive data of transactions and personal medical records is
cautiously maintained, DDL can facilitate the collaboration among these
institutes to improve the performance of trained models while protecting the
data privacy of participating clients. In this survey paper, we demonstrate the
technical fundamentals of DDL that benefit many walks of society through
decentralized learning. Furthermore, we offer a comprehensive overview of the
current state-of-the-art in the field by outlining the challenges of DDL and
the most relevant solutions from novel perspectives of communication efficiency
and trustworthiness.
