There has been an increasing interest in translating artificial intelligence (AI) research into
clinically-validated applications to improve the performance, capacity, and efficacy of …

Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …

Federated learning (FL) is an increasingly popular approach for machine learning (ML) in
cases where the training dataset is highly distributed. Clients perform local training on their …

We introduce a new class of attacks on machine learning models. We show that an
adversary who can poison a training dataset can cause models trained on this dataset to …

Federated learning allows distributed users to collaboratively train a model while kee**
each user's data private. Recently, a growing body of work has demonstrated that an …

Artificial intelligence (AI) models are vulnerable to information leakage of their training data,
which can be highly sensitive, for example, in medical imaging. Privacy-enhancing …

Despite significant improvements over the last few years, cloud-based healthcare
applications continue to suffer from poor adoption due to their limitations in meeting stringent …

Secure aggregation is a cryptographic protocol that securely computes the aggregation of its
inputs. It is pivotal in kee** model updates private in federated learning. Indeed, the use of …

Federated learning (FL) enables multiple clients to collaboratively train a model with the
coordination of a central server. Although FL improves data privacy via kee** each client's …

Background Artificial intelligence (AI) models are increasingly used in the medical domain.
However, as medical data is highly sensitive, special precautions to ensure its protection are …