In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …

The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

Most existing machine learning classifiers are highly vulnerable to adversarial examples. An
adversarial example is a sample of input data which has been modified very slightly in a way …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Byzantine-robust federated learning aims to enable a service provider to learn an accurate
global model when a bounded number of clients are malicious. The key idea of existing …

In federated learning, multiple client devices jointly learn a machine learning model: each
client device maintains a local model for its local training dataset, while a master device …

Deep neural networks and machine-learning algorithms are pervasively used in several
applications, ranging from computer vision to computer security. In most of these …

Data poisoning is an attack on machine learning models wherein the attacker adds
examples to the training set to manipulate the behavior of the model at test time. This paper …

Once users have shared their data online, it is generally difficult for them to revoke access
and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because …

Deep neural networks (DNNs) provide excellent performance across a wide range of
classification tasks, but their training requires high computational resources and is often …