Abstract: Secure aggregation becomes a major solution to providing privacy for federated learning. Secure aggregation for mobile devices typically relies on Shamir secret sharing (SSS) to achieve ...

Abstract: Hierarchical federated learning shows excellent potential for communication-computation trade-offs and reliable data privacy protection by introducing edge-cloud collaboration. Considering ...

Abstract: Multipoint dynamic aggregation (MPDA) is a multirobot task allocation problem, which requires the collaborative scheduling of multiple robots to complete time-varying tasks distributed on a ...

Abstract: Zero-shot learning (ZSL) aims to recognize objects from unseen classes only based on labeled images from seen classes. Most existing ZSL methods focus on optimizing feature spaces or ...

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: Privacy-preserving federated learning (PPFL) is vital for Industry 5.0 digital ecosystems due to the increasing number of interconnected devices and the ...

Abstract: Due to the irregular and disordered data structure in 3D point clouds, prior works have focused on designing more sophisticated local representation methods to capture these complex local ...

Abstract: Large-scale datacenter networks are increasingly using in-network aggregation (INA) and remote direct memory access (RDMA) techniques to accelerate deep neural network (DNN) training.

Abstract: Intelligent processing and analysis of satellite video has become one of the research hotspots in the representation of remote sensing, and satellite video super-resolution (SVSR) is an ...

Abstract: The accuracy and efficiency of path planning in off-road environments depend on the construction of off-road environment map information. Previous studies have used the grid method to ...