Abstract: Hierarchical federated learning shows excellent potential for communication-computation trade-offs and reliable data privacy protection by introducing edge-cloud collaboration. Considering ...
Abstract: Point cloud quality assessment (PCQA) is a challenging task due to the inherently disordered nature of points. Existing point-based methods, such as sparse convolution and PointNet, are ...
Abstract: Existing methods for learning 3D point cloud representation often use a single dataset-specific training and testing approach, leading to performance drops due to significant domain shifts ...
Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...
Abstract: Point cloud processing methods exploit local point features and global context through aggregation which does not explicitly model the internal correlations between local and global features ...
Abstract: This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical ...
Abstract: Constrained by imaging systems, hyperspectral images (HSIs) always have a low spatial resolution. Deep learning-based HSI super-resolution methods have achieved impressive results through ...
Abstract: Distributed Machine Learning (DML) is proposed to accelerate machine learning model training by utilizing multiple training nodes to train models in parallel. Recent studies apply emerging ...
Eight widely followed poll aggregators show that President Donald Trump notched his strongest approval ratings, and his smallest disapproval ratings, in January at the beginning of his second term.
Some results have been hidden because they may be inaccessible to you
Show inaccessible results