… epochs in the segmentation task. We show that federated learning improves the generalizability
of … We show that Grad-CAM explanations for classification models trained on segmented …

… a federated transfer learning framework for personalized healthcare [11]. To cope with above
issues, we propose FedHome, a cloudedge federated learning … velocity data segmented for …

… others in the same cluster (same learning task), they can leverage the … federated learning.
For this new framework of clustered federated learning, we propose the Iterative Federated …

… Federated learning (FL) is a distributed machine learning technique that enables
collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving …

… federated learning algorithms. There was no statistically significant difference between
centralized and federated learning … and SUVmean for both federated and centralized techniques. …

… Descent Scaling and Segmented Regression Fine-tuned Federated Learning (GDS-SRFFL) …
by using a Segmented Regression Fine-tuned Mini-batch Federated Learning model to …

… To achieve model personalization for both inside and outside clients in federated learning,
we propose a novel unified FL framework (named IOP-FL) as illustrated in Fig. 2. In this …

… Feddrive: Generalizing federated learning to semantic segmentation in autonomous driving.
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems …

… a federated learning (FL) framework for multi-institutional PET/CT image segmentation. …
Federated learning (FL) has been proposed for distributed training without sharing data between …

… a federated learning manner. • Collaborative Annotation Calibration (CAC) is proposed
to recognize clean labels and correct noisy labels, and mitigate the local drift through inter-client …