151 citations · 347 across the 5 of their papers we have counts for
5 papers
Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
Jiajun Wu, Steve Drew, Fan Dong +2
The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effec…
Data-Free Knowledge Distillation for Heterogeneous Federated Learning
Zhuangdi Zhu, Junyuan Hong, Jiayu Zhou
Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global server iteratively averages the model parameters of local users without accessing their data…
Off-Policy Imitation Learning from Observations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit through the reuse of incomplete resources. Compared to conv…
Transfer Learning in Deep Reinforcement Learning: A Survey
Zhuangdi Zhu, Kaixiang Lin, Anil K. Jain +1
Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fa…
Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations
Zhuangdi Zhu, Kaixiang Lin, Bo Dai +1
Model-free deep reinforcement learning (RL) has demonstrated its superiority on many complex sequential decision-making problems. However, heavy dependence on dense rewards and hig…