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Found 7 papers, 0 papers with code
Defense via Behavior Attestation against Attacks in Connected and Automated Vehicles based Federated Learning Systems
The recent application of Federated Learning algorithms in IOT and Wireless vehicular networks have given rise to newer cyber threats in the mobile environment which hitherto were not present in traditional fixed networks.
Robustness Analysis of Deep Learning Models for Population Synthesis
Results show that the predictive errors of CTGAN have narrower confidence intervals indicating its robustness to multiple datasets of the varying sample sizes when compared to VAE.
eFedDNN: Ensemble based Federated Deep Neural Networks for Trajectory Mode Inference
As the most significant data source in smart mobility systems, GPS trajectories can help identify user travel mode.
On the Initial Behavior Monitoring Issues in Federated Learning
In Federated Learning (FL), a group of workers participate to build a global model under the coordination of one node, the chief.
Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systems
We identified a number of attack strategies conducted by the malicious CAVs to disrupt the training of the global model in vehicular networks.
A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis
In this work, we develop a privacy-by-design generative model for synthesizing the activity diary of the travel population using state-of-art deep learning approaches.
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis
Agent-based transportation modelling has become the standard to simulate travel behaviour, mobility choices and activity preferences using disaggregate travel demand data for entire populations, data that are not typically readily available.
