Harnessing Interference in Content Delivery by Spatial Signal Alignment. - Dongzhu Liu; Kaibin Huang (GLOBECOM, 2017)

Spatial Alignment of Coding and Modulation Helps Content Delivery. - Dongzhu Liu; Kaibin Huang (CoRR, 2017)

Communication, Computing, and Learning on the Edge. - Kaibin Huang; Guangxu Zhu; Changsheng You; Jun Zhang 004; Yuqing Du; Dongzhu Liu (ICCS, 2018)

Towards an Intelligent Edge: Wireless Communication Meets Machine Learning. - Guangxu Zhu; Dongzhu Liu; Yuqing Du; Changsheng You; Jun Zhang 004; Kaibin Huang (CoRR, 2018)

Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio. - Dongzhu Liu; Kaibin Huang (IEEE Trans. Wirel. Commun., 2018)

Wireless Data Acquisition for Edge Learning: Importance-Aware Retransmission. - Dongzhu Liu; Guangxu Zhu; Jun Zhang 004; Kaibin Huang (SPAWC, 2019)

Exploiting Diversity Via Importance-Aware User Scheduling for Fast Edge Learning. - Dongzhu Liu; Guangxu Zhu; Jun Zhang 004; Kaibin Huang (ICC Workshops, 2020)

Toward an Intelligent Edge: Wireless Communication Meets Machine Learning. - Guangxu Zhu; Dongzhu Liu; Yuqing Du; Changsheng You; Jun Zhang 004; Kaibin Huang (IEEE Commun. Mag., 2020)

Privacy for Free: Wireless Federated Learning via Uncoded Transmission With Adaptive Power Control. - Dongzhu Liu; Osvaldo Simeone (IEEE J. Sel. Areas Commun., 2021)

Data-Importance Aware User Scheduling for Communication-Efficient Edge Machine Learning. - Dongzhu Liu; Guangxu Zhu; Jun Zhang 004; Kaibin Huang (IEEE Trans. Cogn. Commun. Netw., 2021)

Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission. - Dongzhu Liu; Guangxu Zhu; Qunsong Zeng; Jun Zhang 004; Kaibin Huang (IEEE Trans. Wirel. Commun., 2021)

Leveraging Channel Noise for Sampling and Privacy via Quantized Federated Langevin Monte Carlo. - Yunchuan Zhang; Dongzhu Liu; Osvaldo Simeone (SPAWC, 2022)

Channel-Driven Monte Carlo Sampling for Bayesian Distributed Learning in Wireless Data Centers. - Dongzhu Liu; Osvaldo Simeone (IEEE J. Sel. Areas Commun., 2022)

Joint Compression and Deadline Optimization for Communication-Efficient Federated Edge Learning. - Maojun Zhang; Zhijie Cai; Dongzhu Liu; Richeng Jin; Guangxu Zhu; Caijun Zhong (GLOBECOM (Workshops), 2023)

Bayesian Over-the-Air FedAvg via Channel Driven Stochastic Gradient Langevin Dynamics. - Boning Zhang; Dongzhu Liu; Osvaldo Simeone; Guangxu Zhu (GLOBECOM, 2023)

Over-the-Air Federated Edge Learning with Error-Feedback One-Bit Quantization and Power Control. - Yuding Liu; Dongzhu Liu; Guangxu Zhu; Qingjiang Shi; Caijun Zhong (CoRR, 2023)

Joint Compression and Deadline Optimization for Wireless Federated Learning. - Maojun Zhang; Yang Li 049; Dongzhu Liu; Richeng Jin; Guangxu Zhu; Caijun Zhong (CoRR, 2023)

Task-Oriented Integrated Sensing, Computation and Communication for Wireless Edge AI. - Hong Xing; Guangxu Zhu; Dongzhu Liu; Haifeng Wen; Kaibin Huang; Kaishun Wu (IEEE Netw., 2023)

Wireless Federated Langevin Monte Carlo: Repurposing Channel Noise for Bayesian Sampling and Privacy. - Dongzhu Liu; Osvaldo Simeone (IEEE Trans. Wirel. Commun., 2023)

Low-Rank Gradient Compression with Error Feedback for MIMO Wireless Federated Learning. - Mingzhao Guo; Dongzhu Liu; Osvaldo Simeone; Dingzhu Wen (CoRR, 2024)

Channel Capacity-Aware Distributed Encoding for Multi-View Sensing and Edge Inference. - Mingjie Yang; Guangming Liang; Dongzhu Liu; Lei Zhang; Kaibin Huang (CoRR, 2024)

Joint Compression and Deadline Optimization for Wireless Federated Learning. - Maojun Zhang; Yang Li 049; Dongzhu Liu; Richeng Jin; Guangxu Zhu; Caijun Zhong; Tony Q. S. Quek (IEEE Trans. Mob. Comput., 2024)

Personalizing Low-Rank Bayesian Neural Networks Via Federated Learning. - Boning Zhang; Dongzhu Liu; Osvaldo Simeone; Guanchu Wang; Dimitrios Pezaros; Guangxu Zhu (AISTATS, 2025)

Efficient Wireless Federated Learning via Low-Rank Gradient Factorization. - Mingzhao Guo; Dongzhu Liu; Osvaldo Simeone; Dingzhu Wen (IEEE Trans. Veh. Technol., 2025)