55 citations · 84 across the 19 of their papers we have counts for
20 papers
Source Entropy-Guided Adaptive Transmission for Communication-Driven Multi-View Sensing
Mingjie Yang, Guangming Liang, Dongzhu Liu +3
Communication-driven multi-view sensing relies on routine communication transmissions for sensing acquisition, while the resulting sensing data at distributed devices must be uploa…
Federated Martingale Posterior Samping
Boning Zhang, Matteo Zecchin, Mingzhao Guo +2
Federated Bayesian neural networks require fixing a prior on the model parameters, which is notoriously difficult, and misspecification of this prior can severely degrade accuracy…
Null-Space Flow Matching for MIMO Channel Estimation in Latency-Constrained Systems
Junjie Zhao, Guangming Liang, Xiaonan Liu +1
Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems. While advanced deep generative mo…
Semantic-Aware Command and Control Transmission for Multi-UAVs
Boya Li, Xiaonan Liu, Dongzhu Liu +2
Uncrewed aerial vehicles (UAVs) have played an important role in the low-altitude economy and have been used in various applications. However, with the increasing number of UAVs an…
Environment-Aware Channel Inference via Cross-Modal Flow: From Multimodal Sensing to Wireless Channels
Guangming Liang, Mingjie Yang, Dongzhu Liu +2
Accurate channel state information (CSI) underpins reliable and efficient wireless communication. However, acquiring CSI via pilot estimation incurs substantial overhead, especiall…
Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning
Boning Zhang, Dongzhu Liu, Osvaldo Simeone +3
To support real-world decision-making, it is crucial for models to be well-calibrated, i.e., to assign reliable confidence estimates to their predictions. Uncertainty quantificatio…
Efficient Wireless Federated Learning via Low-Rank Gradient Factorization
Mingzhao Guo, Dongzhu Liu, Osvaldo Simeone +1
This paper presents a novel gradient compression method for federated learning (FL) in wireless systems. The proposed method centers on a low-rank matrix factorization strategy for…
Joint User Association and Resource Allocation for Adaptive Semantic Communication in 5G and Beyond Networks
Xingqiu He, Chaoqun You, Zihan Chen +4
Semantic communication (SemCom) has emerged as a promising paradigm that leverages Deep Neural Networks (DNNs) to extract task-relevant information, thereby substantially reducing…
Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI
Hong Xing, Guangxu Zhu, Dongzhu Liu +3
With the advent of emerging IoT applications such as autonomous driving, digital-twin and metaverse etc. featuring massive data sensing, analyzing and inference as well critical la…
Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics
Boning Zhang, Dongzhu Liu, Osvaldo Simeone +1
The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that…
Joint Compression and Deadline Optimization for Wireless Federated Learning
Maojun Zhang, Yang Li, Dongzhu Liu +4
Federated edge learning (FEEL) is a popular distributed learning framework for privacy-preserving at the edge, in which densely distributed edge devices periodically exchange model…
Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control
Yuding Liu, Dongzhu Liu, Guangxu Zhu +2
Over-the-air federated edge learning (Air-FEEL) is a communication-efficient framework for distributed machine learning using training data distributed at edge devices. This framew…
Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo
Yunchuan Zhang, Dongzhu Liu, Osvaldo Simeone
For engineering applications of artificial intelligence, Bayesian learning holds significant advantages over standard frequentist learning, including the capacity to quantify uncer…
Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy
Dongzhu Liu, Osvaldo Simeone
Most works on federated learning (FL) focus on the most common frequentist formulation of learning whereby the goal is minimizing the global empirical loss. Frequentist learning, h…
Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers
Dongzhu Liu, Osvaldo Simeone
Conventional frequentist learning, as assumed by existing federated learning protocols, is limited in its ability to quantify uncertainty, incorporate prior knowledge, guide active…
Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control
Dongzhu Liu, Osvaldo Simeone
Federated Learning (FL) refers to distributed protocols that avoid direct raw data exchange among the participating devices while training for a common learning task. This way, FL…
Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning
Dongzhu Liu, Guangxu Zhu, Jun Zhang +1
With the prevalence of intelligent mobile applications, edge learning is emerging as a promising technology for powering fast intelligence acquisition for edge devices from distrib…
Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission
Dongzhu Liu, Guangxu Zhu, Jun Zhang +1
By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, whi…
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Guangxu Zhu, Dongzhu Liu, Yuqing Du +3
The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Thing…
Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio
Dongzhu Liu, Kaibin Huang
Multimedia content especially videos is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge has emerged to be a soluti…