Xu Chen

TL;DR: In this article, a game theoretic approach for computation offloading in a distributed manner was adopted to solve the multi-user offloading problem in a multi-channel wireless interference environment.

TL;DR: This paper designs a distributed computation offloading algorithm that can achieve a Nash equilibrium, derive the upper bound of the convergence time, and quantify its efficiency ratio over the centralized optimal solutions in terms of two important performance metrics.

TL;DR: A comprehensive survey of the recent research efforts on edge intelligence can be found in this paper, where the authors review the background and motivation for AI running at the network edge and provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the edge.

TL;DR: In this paper, the authors proposed to integrate the Deep Reinforcement Learning techniques and Federated Learning framework with mobile edge systems for optimizing mobile edge computing, caching and communication, and designed 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.

TL;DR: This paper designs a decentralized computation offloading mechanism that can achieve a Nash equilibrium of the game and quantify its efficiency ratio over the centralized optimal solution and demonstrates that the proposed mechanism can achieve efficient computation off loading performance and scale well as the system size increases.