11 citations · 45 across the 14 of their papers we have counts for
4 papers · 1 filter
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?
Songyang Han, Sanbao Su, Sihong He +4
Various methods for Multi-Agent Reinforcement Learning (MARL) have been developed with the assumption that agents' policies are based on accurate state information. However, polici…
Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios
Zhili Zhang, Songyang Han, Jiangwei Wang +1
Communication technologies enable coordination among connected and autonomous vehicles (CAVs). However, it remains unclear how to utilize shared information to improve the safety a…
Uncertainty Quantification of Collaborative Detection for Self-Driving
Sanbao Su, Yiming Li, Sihong He +4
Sharing information between connected and autonomous vehicles (CAVs) fundamentally improves the performance of collaborative object detection for self-driving. However, CAVs still…
A Secure and Efficient Federated Learning Framework for NLP
Jieren Deng, Chenghong Wang, Xianrui Meng +7
In this work, we consider the problem of designing secure and efficient federated learning (FL) frameworks. Existing solutions either involve a trusted aggregator or require heavyw…