pfedlvm: A large vision model (lvm)-driven and latent feature-based personalized federated learning framework in autonomous driving
Deep learning-based Autonomous Driving (AD) models often exhibit poor generalization
due to data heterogeneity in an ever domain-shifting environment. While Federated …
due to data heterogeneity in an ever domain-shifting environment. While Federated …
Fedrc: A rapid-converged hierarchical federated learning framework in street scene semantic understanding
Street Scene Semantic Understanding (denoted as TriSU) is a crucial but complex task for
world-wide distributed autonomous driving (AD) vehicles (eg, Tesla). Its inference model …
world-wide distributed autonomous driving (AD) vehicles (eg, Tesla). Its inference model …
Edge accelerated robot navigation with collaborative motion planning
Low-cost distributed robots suffer from limited onboard computing power, resulting in
excessive computation time when navigating in cluttered environments. This article presents …
excessive computation time when navigating in cluttered environments. This article presents …
A hierarchical federated learning framework for collaborative quality defect inspection in construction
Recent advancements in robotics and deep learning (DL) have made it possible to
implement robots in civil infrastructures' quality defect inspection. Robots can reduce human …
implement robots in civil infrastructures' quality defect inspection. Robots can reduce human …
Fast-convergent and communication-alleviated heterogeneous hierarchical federated learning in autonomous driving
Street Scene Semantic Understanding (denoted as TriSU) is a complex task for autonomous
driving (AD). However, inference model trained from data in a particular geographical region …
driving (AD). However, inference model trained from data in a particular geographical region …
An adverse weather-immune scheme with unfolded regularization and foundation model knowledge distillation for street scene understanding
Various adverse weather conditions pose a significant challenge to autonomous driving
(AD) perception. A common strategy is to minimize the disparity between images captured in …
(AD) perception. A common strategy is to minimize the disparity between images captured in …
Edge Accelerated Robot Navigation with Hierarchical Motion Planning
Low-cost autonomous robots suffer from limited onboard computing power, resulting in
excessive computation time when navigating in cluttered environments. This paper presents …
excessive computation time when navigating in cluttered environments. This paper presents …
A decentralized asynchronous federated learning framework for edge devices
B Wang, Z Tian, J Ma, W Zhang, W She… - Future Generation …, 2024 - Elsevier
The traditional synchronous federated learning framework ensures global model
consistency and accuracy. However, it is limited by the computational power differences …
consistency and accuracy. However, it is limited by the computational power differences …
Stabilizing and Accelerating Federated Learning on Heterogeneous Data With Partial Client Participation
Federated learning (FL) commonly encourages the clients to perform multiple local updates
before the global aggregation, thus avoiding frequent model exchanges and relieving the …
before the global aggregation, thus avoiding frequent model exchanges and relieving the …
Real-World Implementation and Performance Analysis of Distributed Learning Frameworks for 6G IoT Applications
This paper explores the practical implementation and performance analysis of distributed
learning (DL) frameworks on various client platforms, responding to the dynamic landscape …
learning (DL) frameworks on various client platforms, responding to the dynamic landscape …