4 citations · 9 across the 6 of their papers we have counts for
6 papers
Hyperbolic Contrastive Learning for Hierarchical 3D Point Cloud Embedding
Yingjie Liu, Pengyu Zhang, Ziyao He +3
Hyperbolic spaces allow for more efficient modeling of complex, hierarchical structures, which is particularly beneficial in tasks involving multi-modal data. Although hyperbolic g…
When Foresight Pruning Meets Zeroth-Order Optimization: Efficient Federated Learning for Low-Memory Devices
Pengyu Zhang, Yingjie Liu, Yingbo Zhou +4
Although Federated Learning (FL) enables collaborative learning in Artificial Intelligence of Things (AIoT) design, it fails to work on low-memory AIoT devices due to its heavy mem…
Personalized Federated Instruction Tuning via Neural Architecture Search
Pengyu Zhang, Yingbo Zhou, Ming Hu +3
Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still…
Situation-Dependent Causal Influence-Based Cooperative Multi-agent Reinforcement Learning
Xiao Du, Yutong Ye, Pengyu Zhang +3
Learning to collaborate has witnessed significant progress in multi-agent reinforcement learning (MARL). However, promoting coordination among agents and enhancing exploration capa…
AdapterFL: Adaptive Heterogeneous Federated Learning for Resource-constrained Mobile Computing Systems
Ruixuan Liu, Ming Hu, Zeke Xia +5
Federated Learning (FL) enables collaborative learning of large-scale distributed clients without data sharing. However, due to the disparity of computing resources among massive m…
CyclicFL: A Cyclic Model Pre-Training Approach to Efficient Federated Learning
Pengyu Zhang, Yingbo Zhou, Ming Hu +2
Federated learning (FL) has been proposed to enable distributed learning on Artificial Intelligence Internet of Things (AIoT) devices with guarantees of high-level data privacy. Si…