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AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning
In addition, the single-vehicle autonomous driving systems lack of the ability of collaboration and negotiation with other vehicles, which is crucial for the safety and efficiency of autonomous driving systems.
ESFL: Efficient Split Federated Learning over Resource-Constrained Heterogeneous Wireless Devices
Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data.
Collaborative Perception for Connected and Autonomous Driving: Challenges, Possible Solutions and Opportunities
Autonomous driving has attracted significant attention from both academia and industries, which is expected to offer a safer and more efficient driving system.
Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices.
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