With the proliferation of Beyond 5G (B5G) communication systems and heterogeneous
networks, mobile broadband users are generating massive volumes of data that undergo …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …

On-device ML introduces new security challenges: DNN models become white-box
accessible to device users. Based on white-box information, adversaries can conduct …

Artificial Intelligence (AI) fosters enormous business opportunities that build and utilize
private AI models. Implementing AI models at scale and ensuring cost-effective production of …

Privacy and security-related concerns are growing as machine learning reaches diverse
application domains. The data holders want to train or infer with private data while exploiting …

Data privacy is of great concern in cloud machine-learning service platforms when sensitive
data are exposed to service providers. While private computing environments (eg secure …

As machine learning and artificial intelligence (ML/AI) are becoming more popular and
advanced, there is a wish to turn sensitive data into valuable information via ML/AI …

Major cloud service providers with well-equipped infrastructure, experienced machine
learning (ML) expertise, and enriched training datasets are building ML-as-a-Service …

Speech emotion recognition (SER) processes speech signals to detect and characterize
expressed perceived emotions. Many SER application systems often acquire and transmit …