Abstract Machine Learning (ML) models are ubiquitous in real-world applications and are a
constant focus of research. Modern ML models have become more complex, deeper, and …

Video analytics with deep learning techniques has generated immense interest in academia
and industry, captivating minds with its transformative potential. Deep learning techniques …

Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty
quantification in machine learning since it can be easily applied as a post-processing step to …

Privacy estimation techniques for differentially private (DP) algorithms are useful for
comparing against analytical bounds, or to empirically measure privacy loss in settings …

Federated learning (FL) is a framework for machine learning across heterogeneous client
devices in a privacy-preserving fashion. To date, most FL algorithms learn a" global" server …

Federated learning (FL) is creating a paradigm shift in machine learning by directing the
focus of model training to where the data actually exist. Instead of drawing all data into a …

The spread of IoT-based systems presents several potential benefits to society but still has
crucial challenges in different research areas. From the software development point of view …

Cross-device federated learning (FL) is a technique that trains a model on data distributed
across typically millions of edge devices without data leaving the devices. SGD is the …

The large amount of sensitive personal information used in deep learning models has
attracted considerable attention for privacy security. Sensitive data may be memorialized or …

In this article, we present a detailed review of current practices and state-of-the-art
methodologies in the field of differential privacy (DP), with a focus of advancing DP's …