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Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition
However, for machine learning-based traffic sign recognition on the Internet of Vehicles (IoV), a large amount of traffic sign data from distributed vehicles is needed to be gathered in a centralized server for model training, which brings serious privacy leakage risk because of traffic sign data containing lots of location privacy information.
FedParking: A Federated Learning based Parking Space Estimation with Parked Vehicle assisted Edge Computing
In PVEC, different PLOs recruit PVs as edge computing nodes for offloading services through an incentive mechanism, which is designed according to the computation demand and parking capacity constraints derived from FedParking.
Robust Output Regulation and Reinforcement Learning-based Output Tracking Design for Unknown Linear Discrete-Time Systems
To remove such a requirement, an off-policy reinforcement learning algorithm is proposed using only the measured output data along the trajectories of the system and the reference output.
Dynamical Systems
Deep Approximately Orthogonal Nonnegative Matrix Factorization for Clustering
Nonnegative Matrix Factorization (NMF) is a widely used technique for data representation.
