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20132021
most citedFast Convergence Algorithm for Analog Federated Learning

6 citations · 17 across the 10 of their papers we have counts for

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8 papers · 1 filter

cs.IT2021

Mean-Field Approximation based Scheduling for Broadcast Channels with Massive Receivers

Changkun Li, Wei Chen, Khaled B. Letaief

The emerging Industrial Internet of Things (IIoT) is driving an ever increasing demand for providing low latency services to massive devices over wireless channels. As a result, ho…

cs.IT20206 cited

Fast Convergence Algorithm for Analog Federated Learning

Shuhao Xia, Jingyang Zhu, Yuhan Yang +3

In this paper, we consider federated learning (FL) over a noisy fading multiple access channel (MAC), where an edge server aggregates the local models transmitted by multiple end d…

cs.IT2020

Computation Offloading in Heterogeneous Mobile Edge Computing with Energy Harvesting

Tian Zhang, Wei Chen

Energy harvesting aided mobile edge computing (MEC) has gained much attention for its widespread application in the computation-intensive, latency-sensitive and energy-hungry scena…

cs.IT2019

Delay-Optimal and Energy-Efficient Communications with Markovian Arrivals

Xiaoyu Zhao, Wei Chen, Joohyun Lee +1

In this paper, delay-optimal and energy-efficient communication is studied for a single link under Markov random arrivals. We present the optimal tradeoff between delay and power o…

cs.IT2019

Queue-Aware Variable-Length Coding for Ultra Reliable Low Latency Communications with Random Arrival

Xiaoyu Zhao, Wei Chen

With the phenomenal growth of the Internet of Things (IoT), Ultra Reliable Low Latency Communications (URLLC) has potentially been the enabler to guarantee the stringent requiremen…

cs.IT2018

Joint Queue-Aware and Channel-Aware Delay Optimal Scheduling of Arbitrarily Bursty Traffic over Multi-State Time-Varying Channels

Meng Wang, Juan Liu, Wei Chen +1

This paper is motivated by the observation that the average queueing delay can be decreased by sacrificing power efficiency in wireless communications. In this sense, we naturally…