In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning

TL;DR

This paper introduces 'In-Edge AI', a framework that combines federated learning and deep reinforcement learning to enhance mobile edge computing, caching, and communication, achieving near-optimal performance with low overhead.

Contribution

The paper proposes a novel 'In-Edge AI' framework that integrates federated learning and deep reinforcement learning for intelligent, adaptive optimization of mobile edge systems.

Findings

Near-optimal system performance achieved

Low communication overhead in learning process

System is cognitive and adaptable to mobile networks

Abstract

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".