220 citations · 609 across the 49 of their papers we have counts for
8 papers · 1 filter
ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting
Zepu Wang, Yuqi Nie, Peng Sun +3
The criticality of prompt and precise traffic forecasting in optimizing traffic flow management in Intelligent Transportation Systems (ITS) has drawn substantial scholarly focus. S…
Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity
Xuefeng Han, Jun Li, Wen Chen +4
With the rapid proliferation of smart mobile devices, federated learning (FL) has been widely considered for application in wireless networks for distributed model training. Howeve…
Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey
Yulong Wang, Tong Sun, Shenghong Li +4
Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the…
Amplitude-Varying Perturbation for Balancing Privacy and Utility in Federated Learning
Xin Yuan, Wei Ni, Ming Ding +3
While preserving the privacy of federated learning (FL), differential privacy (DP) inevitably degrades the utility (i.e., accuracy) of FL due to model perturbations caused by DP no…
Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
Zongze Li, Shuai Wang, Qingfeng Lin +4
Reconfigurable intelligent surfaces (RISs) have a revolutionary capability to customize the radio propagation environment for wireless networks. To fully exploit the advantages of…
Contextual Model Aggregation for Fast and Robust Federated Learning in Edge Computing
Hung T. Nguyen, H. Vincent Poor, Mung Chiang
Federated learning is a prime candidate for distributed machine learning at the network edge due to the low communication complexity and privacy protection among other attractive p…
Federated Stochastic Gradient Descent Begets Self-Induced Momentum
Howard H. Yang, Zuozhu Liu, Yaru Fu +2
Federated learning (FL) is an emerging machine learning method that can be applied in mobile edge systems, in which a server and a host of clients collaboratively train a statistic…
BScNets: Block Simplicial Complex Neural Networks
Yuzhou Chen, Yulia R. Gel, H. Vincent Poor
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial…