Federated Ensemble

Federated ensemble learning combines the privacy-preserving benefits of federated learning with the improved accuracy and robustness of ensemble methods. Current research focuses on applying this approach to various model architectures, including Bayesian networks, convolutional neural networks (CNNs), and YOLOv5 for object detection, often addressing challenges like data imbalance and distribution shifts across decentralized datasets. This approach is particularly significant for applications like healthcare, where sensitive data necessitates privacy-preserving training, and improved model performance is crucial for reliable diagnoses and predictions.