What question did this study set out to answer?

The aim is to enhance mobile edge systems by integrating deep reinforcement learning and federated learning for better performance.

July 24, 2019Open Access

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

XWXiaofei WangTongji University YHYiwen HanEast China Normal University CWChenyang WangState Grid Corporation of China (China)

Key Points

The aim is to enhance mobile edge systems by integrating deep reinforcement learning and federated learning for better performance.
Developed the In-Edge AI framework integrating deep reinforcement learning with federated learning.
Evaluated the system's performance regarding communication efficiency and learning overhead.
Implemented dynamic optimization techniques at system and application levels.
In-Edge AI achieved near-optimal performance with significantly reduced learning overhead.
The framework effectively improved content delivery and user experience in mobile communications.
Challenges and opportunities for future development of In-Edge AI were identified.

Abstract

Recently, along with the rapid development of mobile communication technology, edge computing theory and techniques have been attracting more and more attention 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 content delivery and improve the quality of mobile services. In order to bring more intelligence to 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 mobile edge systems, for optimizing 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 mobile communication systems. Finally, we discuss several related challenges and opportunities for unveiling a promising upcoming future of “In-Edge AI.”

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