activity
20102026
most citedConvergence of Edge Computing and Deep Learning: A Comprehensive Survey

1.5k citations · 13.5k across the 438 of their papers we have counts for

collaborators
Showing 2020Show all

44 papers · 1 filter

eess.SP2020

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

Helin Yang, Zehui Xiong, Jun Zhao +4

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, th…

eess.SP2020★ 3 cited

Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications

Yulan Gao, Chao Yong, Zehui Xiong +3

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the i…

cs.IT2020★ 1 cited

A Stackelberg Game Approach to Resource Allocation for IRS-aided Communications

Yulan Gao, Chao Yong, Zehui Xiong +3

It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter…

cs.DC2020

Collaborative Coded Computation Offloading: An All-pay Auction Approach

Jer Shyuan Ng, Wei Yang Bryan Lim, Sahil Garg +4

As the amount of data collected for crowdsensing applications increases rapidly due to improved sensing capabilities and the increasing number of Internet of Things (IoT) devices,…

cs.LG2020★ 45 cited

Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things

Yi Liu, Ruihui Zhao, Jiawen Kang +3

Federated Edge Learning (FEL) allows edge nodes to train a global deep learning model collaboratively for edge computing in the Industrial Internet of Things (IIoT), which signific…

cs.LG2020★ 1 cited

Poisoning Semi-supervised Federated Learning via Unlabeled Data: Attacks and Defenses

Yi Liu, Xingliang Yuan, Ruihui Zhao +3

Semi-supervised Federated Learning (SSFL) has recently drawn much attention due to its practical consideration, i.e., the clients may only have unlabeled data. In practice, these S…