222 citations · 465 across the 15 of their papers we have counts for
15 papers
Holographic Beamforming for Semantic Communication
Shuhao Zeng, Haobo Zhang, Su Wang +5
Holographic beamforming enabled by metamaterial antennas has been proposed to facilitate spatial multiplexing at low hardware cost and low power consumption. However, existing holo…
Towards Serverless Semi-Decentralized Federated Learning with Heterogeneous Optimizers
Su Wang, Mung Chiang, H. Vincent Poor
We investigate cluster formation, involving the number and composition of clusters, in decentralized federated learning (FL) with heterogeneous machine learning (ML) optimizers. Wh…
Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks
Su Wang, Mung Chiang, H. Vincent Poor
We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/fog devices contain separate da…
Device Sampling and Resource Optimization for Federated Learning in Cooperative Edge Networks
Su Wang, Roberto Morabito, Seyyedali Hosseinalipour +2
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers
Su Wang, Rajeev Sahay, Adam Piaseczny +1
Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversaria…
Multi-Source to Multi-Target Decentralized Federated Domain Adaptation
Su Wang, Seyyedali Hosseinalipour, Christopher G. Brinton
Heterogeneity across devices in federated learning (FL) typically refers to statistical (e.g., non-i.i.d. data distributions) and resource (e.g., communication bandwidth) dimension…
Towards Cooperative Federated Learning over Heterogeneous Edge/Fog Networks
Su Wang, Seyyedali Hosseinalipour, Vaneet Aggarwal +4
Federated learning (FL) has been promoted as a popular technique for training machine learning (ML) models over edge/fog networks. Traditional implementations of FL have largely ne…
How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?
Su Wang, Rajeev Sahay, Christopher G. Brinton
There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating…
GenAD: General Representations of Multivariate Time Seriesfor Anomaly Detection
Xiaolei Hua, Lin Zhu, Shenglin Zhang +5
The reliability of wireless base stations in China Mobile is of vital importance, because the cell phone users are connected to the stations and the behaviors of the stations are d…
Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks
Seyyedali Hosseinalipour, Su Wang, Nicolo Michelusi +4
Federated learning (FedL) has emerged as a popular technique for distributing model training over a set of wireless devices, via iterative local updates (at devices) and global agg…
threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learning
Su Wang, Zhiliang Wang, Tao Zhou +6
Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent Threats (APT), are commonly adopted by modern attackers. Recent studies propose leveraging…
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Dongqi Han, Zhiliang Wang, Wenqi Chen +6
Unsupervised Deep Learning (DL) techniques have been widely used in various security-related anomaly detection applications, owing to the great promise of being able to detect unfo…
UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach
Su Wang, Seyyedali Hosseinalipour, Maria Gorlatova +2
We investigate training machine learning (ML) models across a set of geo-distributed, resource-constrained clusters of devices through unmanned aerial vehicles (UAV) swarms. The pr…
Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementation
Su Wang, Mengyuan Lee, Seyyedali Hosseinalipour +3
The conventional federated learning (FedL) architecture distributes machine learning (ML) across worker devices by having them train local models that are periodically aggregated b…
Network-Aware Optimization of Distributed Learning for Fog Computing
Su Wang, Yichen Ruan, Yuwei Tu +3
Fog computing promises to enable machine learning tasks to scale to large amounts of data by distributing processing across connected devices. Two key challenges to achieving this…