IEEE

Adaptive Model Aggregation in Federated Learning Based on Model Accuracy

Abstract: Federated learning is useful when predicting user preferences due to its ability to keep user data private. As such, certain data samples may be more useful than others. For instance, with a ...
IEEE

Batch-Aggregate: Efficient Aggregation for Private Federated Learning in VANETs

Abstract: Federated learning (FL) in Vehicular Ad-hoc Networks (VANETs) enables vehicles to collaboratively train machine learning models by aggregating local gradients without revealing the training ...