29 citations · 68 across the 11 of their papers we have counts for
12 papers
Learning Channel Capacity with Neural Mutual Information Estimator Based on Message Importance Measure
Zhefan Li, Rui She, Pingyi Fan +2
Channel capacity estimation plays a crucial role in beyond 5G intelligent communications. Despite its significance, this task is challenging for a majority of channels, especially…
Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles
Xiaoxue Yu, Rongpeng Li, Fei Wang +4
Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However,…
RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning
Xingfu Yi, Rongpeng Li, Chenghui Peng +3
The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as…
FedNC: A Secure and Efficient Federated Learning Method with Network Coding
Yuchen Shi, Zheqi Zhu, Pingyi Fan +2
Federated Learning (FL) is a promising distributed learning mechanism which still faces two major challenges, namely privacy breaches and system efficiency. In this work, we reconc…
Stochastic Graph Neural Network-based Value Decomposition for MARL in Internet of Vehicles
Baidi Xiao, Rongpeng Li, Fei Wang +4
Autonomous driving has witnessed incredible advances in the past several decades, while Multi-Agent Reinforcement Learning (MARL) promises to satisfy the essential need of autonomo…
FedLP: Layer-wise Pruning Mechanism for Communication-Computation Efficient Federated Learning
Zheqi Zhu, Yuchen Shi, Jiajun Luo +4
Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and co…
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Zheqi Zhu, Yuchen Shi, Pingyi Fan +2
As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. D…
6G Network AI Architecture for Everyone-Centric Customized Services
Yang Yang, Mulei Ma, Hequan Wu +51
Mobile communication standards were developed for enhancing transmission and network performance by using more radio resources and improving spectrum and energy efficiency. How to…
How global observation works in Federated Learning: Integrating vertical training into Horizontal Federated Learning
Shuo Wan, Jiaxun Lu, Pingyi Fan +3
Federated learning (FL) has recently emerged as a transformative paradigm that jointly train a model with distributed data sets in IoT while avoiding the need for central data coll…
Semantic Communication with Adaptive Universal Transformer
Qingyang Zhou, Rongpeng Li, Zhifeng Zhao +2
With the development of deep learning (DL), natural language processing (NLP) makes it possible for us to analyze and understand a large amount of language texts. Accordingly, we c…
Convergence Analysis and System Design for Federated Learning over Wireless Networks
Shuo Wan, Jiaxun Lu, Pingyi Fan +3
Federated learning (FL) has recently emerged as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their raw data sets. Ho…
Toward Native Artificial Intelligence in 6G Networks: System Design, Architectures, and Paradigms
Jianjun Wu, Rongpeng Li, Xueli An +4
The mobile communication system has transformed to be the fundamental infrastructure to support digital demands from all industry sectors, and 6G is envisioned to go far beyond the…