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Found 5 papers, 1 papers with code
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 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.
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.
