2 citations · 2 across the 5 of their papers we have counts for
5 papers
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…
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…
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…