FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney;University of Washington code FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning ...
The amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can n
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney;University of Washington code FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning ...
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney;University of Washington code FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning ...
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney;University of Washington code FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning ...
FedProto: Federated Prototype Learning over Heterogeneous Devices University of Technology Sydney;University of Washington code FedSoft: Soft Clustered Federated Learning with Proximal Local Updating Carnegie Mellon University Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning ...
It is evident that federated learning with a P2P architecture is well suited for the connected car environment. However, the complexity of the P2P architecture poses challenges compared to the C-S architecture. Moreover, most existing studies on federated learning in IoV with a P2P architecture ar...
The SMOTE oversampling technique is also employed to balance the dataset before model training. Our proposed framework has demonstrated efficiency and effectiveness in enhancing classification performance and prediction accuracy. Keywords: credit card fraud; financial fraud; fraud detection; federated learning...