87 citations · 116 across the 11 of their papers we have counts for
12 papers
TernaryVote: Differentially Private, Communication Efficient, and Byzantine Resilient Distributed Optimization on Heterogeneous Data
Richeng Jin, Yujie Gu, Kai Yue +3
Distributed training of deep neural networks faces three critical challenges: privacy preservation, communication efficiency, and robustness to fault and adversarial behaviors. Alt…
Magnitude Matters: Fixing SIGNSGD Through Magnitude-Aware Sparsification in the Presence of Data Heterogeneity
Richeng Jin, Xiaofan He, Caijun Zhong +3
Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods h…
Graph Neural Network Based Node Deployment for Throughput Enhancement
Yifei Yang, Dongmian Zou, Xiaofan He
The recent rapid growth in mobile data traffic entails a pressing demand for improving the throughput of the underlying wireless communication networks. Network node deployment has…
Multi-UAV Collaborative Sensing and Communication: Joint Task Allocation and Power Optimization
Kaitao Meng, Xiaofan He, Qingqing Wu +1
Compared to a single UAV with limited sensing coverage and communication capability, multi-UAV cooperation is able to provide more effective sensing and transmission (S&T) services…
Spectral Graph Theory Based Resource Allocation for IRS-Assisted Multi-Hop Edge Computing
Huilian Zhang, Xiaofan He, Qingqing Wu +1
The performance of mobile edge computing (MEC) depends critically on the quality of the wireless channels. From this viewpoint, the recently advocated intelligent reflecting surfac…
Communication Efficient Federated Learning with Energy Awareness over Wireless Networks
Richeng Jin, Xiaofan He, Huaiyu Dai
In federated learning (FL), reducing the communication overhead is one of the most critical challenges since the parameter server and the mobile devices share the training paramete…