While machine learning models are usually assumed to always output a prediction, there
also exist extensions in the form of reject options which allow the model to reject inputs …

In federated learning (FL), it is generally assumed that all data are placed at clients in the
beginning of machine learning (ML) optimization (ie, offline learning). However, in many real …

In recent years the applications of machine learning models have increased rapidly, due to
the large amount of available data and technological progress. While some domains like …

In this work, we will consider how privacy for vector quantization models can be broken in a
federated learning environment. We show how a potential attacker can expose data from the …

Federated learning is an efficient methodology to reduce the data transmissions to the
server when working with large amounts of (sensor) data from diverse physical locations …

The increasing availability of sensor technologies enables the use of machine learning
techniques in many applications. Frequently, the systems outperform humans as the sensors …