Federated Generative Models in Medical Imaging: Current Advances, Challenges, and Future Directions
Published in: IEEE Access
Jan 05, 2026
Haroon Mahmood
Zareen Alamgir
Sobia Tariq Javed
Saira Karim
Muhammad Awais
The fusion of Federated Learning (FL) and deep generative models is transforming medical imaging by enabling privacy-preserving and data-efficient machine learning. Training large-scale deep models on radiological imaging data remains challenging due to data scarcity, heterogeneity, and strict privacy constraints that limit data sharing across institutions. FL addresses these challenges by employing collaborative model training that does not expose raw patient data, while generative models synt...
Automatic speech emotion recognition for arabic dialects: a new dataset and machine learning framework
Published in: Cluster Computing
Jan 01, 2026
Automatic Speech Emotion Recognition (ASER) is a critical aspect of affective computing, which detects emotions in speech to facilitate efficient human-computer interaction. An area that has received little attention in previous research is the Algerian Arabic dialect, which is the setting in which this study examines ASER. We introduce a new corpus, Open Your Heart (OYH), which consists of roughly 6.3 hours of emotional spontaneous speech taken from a talk show on television. A wide variety of...