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Found 14 papers, 1 papers with code
Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions.
Automated Federated Pipeline for Parameter-Efficient Fine-Tuning of Large Language Models
Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs).
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.
SmartCooper: Vehicular Collaborative Perception with Adaptive Fusion and Judger Mechanism
Moreover, in the context of collaborative perception, it is important to recognize that not all CAVs contribute valuable data, and some CAV data even have detrimental effects on collaborative perception.
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.
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
To address these challenges, we propose a unified domain generalization framework applicable in both training and inference stages of collaborative perception.
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
To address these issues, we propose ACC-DA, a channel-aware collaborative perception framework to dynamically adjust the communication graph and minimize the average transmission delay while mitigating the side effects from the data heterogeneity.
Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy
In this paper, we propose a novel wireless FL scheme called private federated edge learning with sparsification (PFELS) to provide client-level DP guarantee with intrinsic channel noise while reducing communication and energy overhead and improving model accuracy.
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.
Age-Stratified COVID-19 Spread Analysis and Vaccination: A Multitype Random Network Approach
As a result, age-stratified modeling for COVID-19 dynamics is the key to study how to reduce hospitalizations and mortality from COVID-19.
Towards Energy Efficient Federated Learning over 5G+ Mobile Devices
The continuous convergence of machine learning algorithms, 5G and beyond (5G+) wireless communications, and artificial intelligence (AI) hardware implementation hastens the birth of federated learning (FL) over 5G+ mobile devices, which pushes AI functions to mobile devices and initiates a new era of on-device AI applications.
Energy Efficient Federated Learning over Heterogeneous Mobile Devices via Joint Design of Weight Quantization and Wireless Transmission
To address these challenges, in this paper, we attempt to take FL into the design of future wireless networks and develop a novel joint design of wireless transmission and weight quantization for energy efficient FL over mobile devices.
Secure Transmission by Leveraging Multiple Intelligent Reflecting Surfaces in MISO Systems
Specifically, to guarantee the worst-case achievable secrecy rate among multiple legitimate users, we formulate a max-min problem that can be solved by an alternative optimization method to decouple it into multiple sub-problems.
