Machine learning (ML) is increasingly being adopted in a wide variety of application
domains. Usually, a well-performing ML model relies on a large volume of training data and …

Several studies have used machine learning algorithms to develop intrusion systems (IDS),
which differentiate anomalous behaviours from the normal activities of network systems. Due …

Federated learning (FL) is a distributed machine learning technique that enables multiple
decentralized clients to develop a model collaboratively without exchanging their local data …

While the quality of machine learning services largely relies on the volume of training data,
data regulations such as the General Data Protection Regulation (GDPR) impose stringent …

The wide adoption of Machine Learning to solve a large set of real-life problems came with
the need to collect and process large volumes of data, some of which are considered …

Federated Learning (FL) has been an emerging trend in machine learning and artificial
intelligence. It allows multiple participants to collaboratively train a better global model and …

There is great demand for scalable, secure, and efficient privacy-preserving machine
learning models that can be trained over distributed data. While deep learning models …

Neural network (NN) inference services enrich many applications, like image classification,
object recognition, facial verification, and more. These NN inference services are …

Algorithmic discrimination is one of the significant concerns in applying machine learning
models to a real-world system. Many researchers have focused on developing fair machine …

Federated Learning (FL) is an approach to machine learning in which the training data are
not managed centrally. Data are retained by data parties that participate in the FL process …