Jun 16, 2021 · We introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter.
Based on the simulation of virtual autonomous driving based on real-world road images, it is verified that our proposes scheme can reduce 73.7 % training loss.
FPDGD preserves user privacy by using the decentralised nature of federated learning to facilitate the cooperative training of OLTR models across numerous ...
Therefore, we introduce FL into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model ...
Mar 18, 2024 · This shift empowers CAVs to exhibit vastly improved driving behaviors, leveraging shared information for enhanced accuracy, reliability, and ...
FL is introduced into autonomous driving to preserve vehicular privacy by keeping original data in a local vehicle and sharing the training model parameter ...
Jul 23, 2024 · Federated learning advances privacy-preserving distributed machine learning by aggregating the model parameter updates from individual devices ...
Aug 23, 2024 · This paper presents a comprehensive study of 3D point cloud Federated Few-Shot Learning (3DFFL), focusing on addressing challenges such as limited data ...
Jun 13, 2024 · This paper proposes a federated reinforcement learning framework designed to establish a privacy-preserving knowledge-sharing strategy.
Mar 8, 2023 · In this paper, we relax the data collection requirement and enhance uncertainty-awareness by using Federated Learning on Connected Autonomous Vehicles.