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Found 16 papers, 2 papers with code
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.
CG-SLAM: Efficient Dense RGB-D SLAM in a Consistent Uncertainty-aware 3D Gaussian Field
Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM).
AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks
In this paper, we provide a convergence analysis of SFL which quantifies the impact of model splitting (MS) and client-side model aggregation (MA) on the learning performance, serving as a theoretical foundation.
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.
FedSN: A Novel Federated Learning Framework over LEO Satellite Networks
To this end, we propose FedSN as a general FL framework to tackle the above challenges, and fully explore data diversity on LEO satellites.
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs.
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.
Optimal Resource Allocation for U-Shaped Parallel Split Learning
In this paper, we develop a novel parallel U-shaped split learning and devise the optimal resource optimization scheme to improve the performance of edge networks.
Split Learning in 6G Edge Networks
With the proliferation of distributed edge computing resources, the 6G mobile network will evolve into a network for connected intelligence.
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.
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer
Further, the proposed FSSDA can be effectively generalized to multi-source domain adaptation scenarios.
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.
