  Recently, along with the rapid development of mobile communication
technology, edge computing theory and techniques have been attracting more and
more attentions from global researchers and engineers, which can significantly
bridge the capacity of cloud and requirement of devices by the network edges,
and thus can accelerate the content deliveries and improve the quality of
mobile services. In order to bring more intelligence to the edge systems,
compared to traditional optimization methodology, and driven by the current
deep learning techniques, we propose to integrate the Deep Reinforcement
Learning techniques and Federated Learning framework with the mobile edge
systems, for optimizing the mobile edge computing, caching and communication.
And thus, we design the "In-Edge AI" framework in order to intelligently
utilize the collaboration among devices and edge nodes to exchange the learning
parameters for a better training and inference of the models, and thus to carry
out dynamic system-level optimization and application-level enhancement while
reducing the unnecessary system communication load. "In-Edge AI" is evaluated
and proved to have near-optimal performance but relatively low overhead of
learning, while the system is cognitive and adaptive to the mobile
communication systems. Finally, we discuss several related challenges and
opportunities for unveiling a promising upcoming future of "In-Edge AI".
