27 citations · 41 across the 9 of their papers we have counts for
9 papers
Robust Segmented Analog Broadcast Design to Accelerate Wireless Federated Learning
Chong Zhang, Ben Liang, Min Dong +2
We consider downlink broadcast design for federated learning (FL) in a wireless network with imperfect channel state information (CSI). Aiming to reduce transmission latency, we pr…
SegOTA: Accelerating Over-the-Air Federated Learning with Segmented Transmission
Chong Zhang, Min Dong, Ben Liang +2
Federated learning (FL) with over-the-air computation efficiently utilizes the communication resources, but it can still experience significant latency when each device transmits a…
Improving Wireless Federated Learning via Joint Downlink-Uplink Beamforming over Analog Transmission
Chong Zhang, Min Dong, Ben Liang +2
Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless li…
Uplink Over-the-Air Aggregation for Multi-Model Wireless Federated Learning
Chong Zhang, Min Dong, Ben Liang +2
We propose an uplink over-the-air aggregation (OAA) method for wireless federated learning (FL) that simultaneously trains multiple models. To maximize the multi-model training con…
Fast Group Scheduling for Downlink Large-Scale Multi-Group Multicast Beamforming
Chong Zhang, Min Dong, Ben Liang +2
Next-generation wireless networks need to handle massive user access effectively. This paper addresses the problem of joint group scheduling and multicast beamforming for downlink…
Multi-Model Wireless Federated Learning with Downlink Beamforming
Chong Zhang, Min Dong, Ben Liang +2
This paper studies the design of wireless federated learning (FL) for simultaneously training multiple machine learning models. We consider round robin device-model assignment and…
Joint Downlink-Uplink Beamforming for Wireless Multi-Antenna Federated Learning
Chong Zhang, Min Dong, Ben Liang +2
We study joint downlink-uplink beamforming design for wireless federated learning (FL) with a multi-antenna base station. Considering analog transmission over noisy channels and up…
Ultra-Low-Complexity Algorithms with Structurally Optimal Multi-Group Multicast Beamforming in Large-Scale Systems
Chong Zhang, Min Dong, Ben Liang
In this work, we propose ultra-low-complexity design solutions for multi-group multicast beamforming in large-scale systems. For the quality-of-service (QoS) problem, by utilizing…
Fast First-Order Algorithm for Large-Scale Max-Min Fair Multi-Group Multicast Beamforming
Chong Zhang, Min Dong, Ben Liang
We propose a first-order fast algorithm for the weighted max-min fair (MMF) multi-group multicast beamforming problem in large-scale systems. Utilizing the optimal multicast beamfo…