A Belief Propagation Approach for Caching Strategy Design in D2D Networks. - Shengqi Hao; Naifu Zhang; Meixia Tao (ICC Workshops, 2018)

Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching. - Naifu Zhang; Kaibin Zheng; Meixia Tao (ICC Workshops, 2018)

Fitness-Aware Coded Multicasting for Decentralized Caching With Finite File Packetization. - Naifu Zhang; Meixia Tao (IEEE Wirel. Commun. Lett., 2018)

Gradient Statistics Aware Power Control for Over-the-Air Federated Learning in Fading Channels. - Naifu Zhang; Meixia Tao (ICC Workshops, 2020)

MQA: Answering the Question via Robotic Manipulation. - Yuhong Deng; Naifu Zhang; Di Guo; Huaping Liu 001; Fuchun Sun 001; Chen Pang; Jing Pang (CoRR, 2020)

Extended Radial Basis Function Controller for Reinforcement Learning. - Nicholas Capel; Naifu Zhang (CoRR, 2020)

Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning. - Naifu Zhang; Meixia Tao; Jia Wang 004 (ISIT, 2021)

LEOC: A Principled Method in Integrating Reinforcement Learning and Classical Control Theory. - Naifu Zhang; Nicholas Capel (L4DC, 2021)

MQA: Answering the Question via Robotic Manipulation. - Yuhong Deng; Di Guo; Xiaofeng Guo; Naifu Zhang; Huaping Liu 001; Fuchun Sun 001 (Robotics: Science and Systems, 2021)

Adaptive Clustering-Based Model Aggregation for Federated Learning with Imbalanced Data. - Dong Wang 058; Naifu Zhang; Meixia Tao (SPAWC, 2021)

An Adaptive Distributed Source Coding Design for Distributed Learning. - Naifu Zhang; Meixia Tao (WCSP, 2021)

HD-eXplosion: visualization of hydrogen-deuterium exchange data as chiclet and volcano plots with statistical filtering. - Naifu Zhang; Xiaohe Yu; Xinchao Zhang; Sheena D'arcy (Bioinform., 2021)

Rate Region for Indirect Multiterminal Source Coding in Federated Learning. - Naifu Zhang; Meixia Tao; Jia Wang 004 (CoRR, 2021)

Gradient Statistics Aware Power Control for Over-the-Air Federated Learning. - Naifu Zhang; Meixia Tao (IEEE Trans. Wirel. Commun., 2021)

Federated Multi-Agent Reinforcement Learning for Collaborative Edge Caching in Content Delivery Networks. - Jialing Chang; Naifu Zhang; Meixia Tao; Hu Tuo (WCSP, 2022)

A Low-Complexity and Adaptive Distributed Source Coding Design for Model Aggregation in Distributed Learning. - Naifu Zhang; Meixia Tao (IEEE Open J. Commun. Soc., 2022)

Fundamental Limits of Communication Efficiency for Model Aggregation in Distributed Learning: A Rate-Distortion Approach. - Naifu Zhang; Meixia Tao; Jia Wang 004; Fan Xu 001 (IEEE Trans. Commun., 2023)

Coded Over-the-Air Computation for Model Aggregation in Federated Learning. - Naifu Zhang; Meixia Tao; Jia Wang 004; Shuo Shao 001 (IEEE Commun. Lett., 2023)

Knowledge Selection and Local Updating Optimization for Federated Knowledge Distillation With Heterogeneous Models. - Dong Wang 058; Naifu Zhang; Meixia Tao; Xu Chen 004 (IEEE J. Sel. Top. Signal Process., 2023)

Event-Based Asymptotic Tracking Control for Constrained MIMO Nonlinear Systems via a Single-Parameter Adaptation Method. - Zhiguang Feng; Rui-Bing Li; Naifu Zhang; Ligang Wu 001 (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs. - Yichun Yin; Wenyong Huang; Kaikai Song; Yehui Tang; Xueyu Wu; Wei Guo 006; Peng Guo; Yaoyuan Wang; Xiaojun Meng; Yasheng Wang; Dong Li 016; Can Chen; et al. (CoRR, 2025)

Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs. - Yehui Tang; Yichun Yin; Yaoyuan Wang; Hang Zhou; Yu Pan; Wei Guo 006; Zhiguo Zhang; Miao Rang; Fangcheng Liu; Naifu Zhang; Binghan Li; Yonghan Dong; et al. (CoRR, 2025)