GitHub

FedCFA: Alleviating Simpson’s Paradox in Model Aggregation with Counterfactual Federated Learning

Abstract: Federated learning (FL) is a promising technology for data privacy and distributed optimization, but it suffers from data imbalance and heterogeneity among clients. Existing FL methods try ...
IEEE

Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Abstract: Optical imagery ship detection has achieved significant developments recently. However, accurate detection in complex scenes and for different-scale ships remains a vital challenge. To solve ...
IEEE

FedRDF: A Robust and Dynamic Aggregation Function Against Poisoning Attacks in Federated Learning

Abstract: Federated Learning (FL) represents a promising approach to typical privacy concerns associated with centralized Machine Learning (ML) deployments. Despite its well-known advantages, FL is ...