The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Users' privacy is vulnerable at all stages of the deep learning process. Sensitive information
of users may be disclosed during data collection, during training, or even after releasing the …

The artificial intelligence revolution has been spurred forward by the availability of large-
scale datasets. In contrast, the paucity of large-scale medical datasets hinders the …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Existing traffic flow forecasting approaches by deep learning models achieve excellent
success based on a large volume of data sets gathered by governments and organizations …

Federated learning facilitates the collaborative training of models without the sharing of raw
data. However, recent attacks demonstrate that simply maintaining data locality during …

Deep Learning (DL) algorithms based on artificial neural networks have achieved
remarkable success and are being extensively applied in a variety of application domains …

We consider a distributed empirical risk minimization (ERM) optimization problem with
communication efficiency and privacy requirements, motivated by the federated learning …

We introduce the multi-dimensional Skellam mechanism, a discrete differential privacy
mechanism based on the difference of two independent Poisson random variables. To …