22 citations · 47 across the 6 of their papers we have counts for
6 papers
Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach
Naifu Zhang, Meixia Tao, Jia Wang +1
One of the main focuses in distributed learning is communication efficiency, since model aggregation at each round of training can consist of millions to billions of parameters. Se…
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning
Naifu Zhang, Meixia Tao, Jia Wang
One of the main focus in federated learning (FL) is the communication efficiency since a large number of participating edge devices send their updates to the edge server at each ro…
Extended Radial Basis Function Controller for Reinforcement Learning
Nicholas Capel, Naifu Zhang
There have been attempts in reinforcement learning to exploit a priori knowledge about the structure of the system. This paper proposes a hybrid reinforcement learning controller w…
MQA: Answering the Question via Robotic Manipulation
Yuhong Deng, Di Guo, Xiaofeng Guo +3
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given qu…
Gradient Statistics Aware Power Control for Over-the-Air Federated Learning
Naifu Zhang, Meixia Tao
Federated learning (FL) is a promising technique that enables many edge devices to train a machine learning model collaboratively in wireless networks. By exploiting the superposit…
Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching
Naifu Zhang, Kaibin Zheng, Meixia Tao
Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency req…