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 emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

The “Right to be Forgotten” rule in machine learning (ML) practice enables some individual
data to be deleted from a trained model, as pursued by recently developed machine …

Federated learning (FL) relies on a central authority to oversee and aggregate model
updates contributed by multiple participating parties in the training process. This …

Many machine learning techniques rely on plenty of training data. However, data are often
possessed unequally by different entities, with a large proportion of data being held by a …

We propose a solution for user privacy-oriented privacy-preserving data aggregation with
multiple data customers. Most existing state-of-the-art approaches present too much …

Conducting secure computations to protect against malicious adversaries is an emerging
field of research. Current models designed for malicious security typically necessitate the …

With the development of deep learning, people are more and more concerned about the
security of data. Federated learning can solve the problem of data island, but it also brings …

Personalized learning is a proposed approach to address the problem of data heterogeneity
in collaborative machine learning. In a decentralized setting, the two main challenges of …

Federated learning (FL) allows multiple users to collaboratively train global machine
learning models by keeping their data sets local. However, the existing privacy-preserving …