47 citations · 84 across the 10 of their papers we have counts for
5 papers · 1 filter
V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection
Rui Song, Lingjuan Lyu, Wei Jiang +2
Machine learning (ML) has revolutionized transportation systems, enabling autonomous driving and smart traffic services. Federated learning (FL) overcomes privacy constraints by tr…
ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals
Rui Song, Liguo Zhou, Lingjuan Lyu +2
Federated learning enables cooperative training among massively distributed clients by sharing their learned local model parameters. However, with increasing model size, deploying…
Federated Learning Framework Coping with Hierarchical Heterogeneity in Cooperative ITS
Rui Song, Liguo Zhou, Venkatnarayanan Lakshminarasimhan +2
Deep learning is a key approach for the environment perception function of Cooperative Intelligent Transportation Systems (C-ITS) with autonomous vehicles and smart traffic infrast…
Graph Neural Networks for Relational Inductive Bias in Vision-based Deep Reinforcement Learning of Robot Control
Marco Oliva, Soubarna Banik, Josip Josifovski +1
State-of-the-art reinforcement learning algorithms predominantly learn a policy from either a numerical state vector or images. Both approaches generally do not take structural kno…
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers
Brian Hsuan-Cheng Liao, Chih-Hong Cheng, Hasan Esen +1
As an emerging type of Neural Networks (NNs), Transformers are used in many domains ranging from Natural Language Processing to Autonomous Driving. In this paper, we study the robu…