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Found 14 papers, 2 papers with code
On the Impact of Data Heterogeneity in Federated Learning Environments with Application to Healthcare Networks
Federated Learning (FL) allows multiple privacy-sensitive applications to leverage their dataset for a global model construction without any disclosure of the information.
A Secure and Trustworthy Network Architecture for Federated Learning Healthcare Applications
This paper provides an overview of the TRUSTroke FL network infrastructure.
Deep Learning-based Cooperative LiDAR Sensing for Improved Vehicle Positioning
In line with this trend, this paper proposes a novel data-driven cooperative sensing framework, termed Cooperative LiDAR Sensing with Message Passing Neural Network (CLS-MPNN), where spatially-distributed vehicles collaborate in perceiving the environment via LiDAR sensors.
A Tutorial on 5G Positioning
The widespread adoption of the fifth generation (5G) of cellular networks has brought new opportunities for the development of localization-based services.
A Carbon Tracking Model for Federated Learning: Impact of Quantization and Sparsification
Federated Learning (FL) methods adopt efficient communication technologies to distribute machine learning tasks across edge devices, reducing the overhead in terms of data storage and computational complexity compared to centralized solutions.
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative training.
Motion Estimation and Compensation in Automotive MIMO SAR
With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment.
Multi-Beam Automotive SAR Imaging in Urban Scenarios
Automotive synthetic aperture radar (SAR) systems are rapidly emerging as a candidate technological solution to enable a high-resolution environment mapping for autonomous driving.
Residual Motion Compensation in Automotive MIMO SAR Imaging
This paper deals with the analysis, estimation, and compensation of trajectory errors in automotive-based Synthetic Aperture Radar (SAR) systems.
Cooperative Localization and Multitarget Tracking in Agent Networks with the Sum-Product Algorithm
This paper addresses the problem of multitarget tracking using a network of sensing agents with unknown positions.
Fastening the Initial Access in 5G NR Sidelink for 6G V2X Networks
The ever-increasing demand for intelligent, automated, and connected mobility solutions pushes for the development of an innovative sixth Generation (6G) of cellular networks.
Sensor-Aided Beamwidth and Power Control for Next Generation Vehicular Communications
Ultra-reliable low-latency Vehicle-to-Everything (V2X) communications are needed to meet the extreme requirements of enhanced driving applications.
Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems
Next-generation autonomous and networked industrial systems (i. e., robots, vehicles, drones) have driven advances in ultra-reliable, low latency communications (URLLC) and computing.
Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks
Federated learning (FL) is emerging as a new paradigm to train machine learning models in distributed systems.
