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Found 6 papers, 3 papers with code
FedAH: Aggregated Head for Personalized Federated Learning
Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization.
FedPAW: Federated Learning with Personalized Aggregation Weights for Urban Vehicle Speed Prediction
Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ.
FedRAV: Hierarchically Federated Region-Learning for Traffic Object Classification of Autonomous Vehicles
However, considering the complicated traffic environments and driving scenarios, deploying federated learning for autonomous vehicles is inevitably challenged by non-independent and identically distributed (Non-IID) data of vehicles, which may lead to failed convergence and low training accuracy.
A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks
Lots of papers have emerged to investigate the robustness and safety of deep learning models against adversarial attacks.
Physical-World Optical Adversarial Attacks on 3D Face Recognition
2D face recognition has been proven insecure for physical adversarial attacks.
Random Forests and VGG-NET: An Algorithm for the ISIC 2017 Skin Lesion Classification Challenge
This manuscript briefly describes an algorithm developed for the ISIC 2017 Skin Lesion Classification Competition.
