17 citations · 45 across the 8 of their papers we have counts for
5 papers · 1 filter
Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy
Yifan Shi, Kang Wei, Li Shen +4
To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard f…
Improving the Model Consistency of Decentralized Federated Learning
Yifan Shi, Li Shen, Kang Wei +4
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its…
SaFormer: A Conditional Sequence Modeling Approach to Offline Safe Reinforcement Learning
Qin Zhang, Linrui Zhang, Haoran Xu +6
Offline safe RL is of great practical relevance for deploying agents in real-world applications. However, acquiring constraint-satisfying policies from the fixed dataset is non-tri…
Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks
Linrui Zhang, Qin Zhang, Li Shen +3
Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, the…
Penalized Proximal Policy Optimization for Safe Reinforcement Learning
Linrui Zhang, Li Shen, Long Yang +4
Safe reinforcement learning aims to learn the optimal policy while satisfying safety constraints, which is essential in real-world applications. However, current algorithms still s…