29 citations · 68 across the 11 of their papers we have counts for
6 papers · 1 filter
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…