Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Federated analytics (FA) is a privacy-preserving framework for computing data analytics
over multiple remote parties (eg, mobile devices) or silo-ed institutional entities (eg …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

In a vertical federated learning (VFL) system consisting of a central server and many
distributed clients, the training data are vertically partitioned such that different features are …

We propose SwiftAgg+, a novel secure aggregation protocol for federated learning systems,
where a central server aggregates local models of distributed users, each of size, trained on …

We present two novel federated learning (FL) schemes that mitigate the effect of straggling
devices by introducing redundancy on the devices' data across the network. Compared to …

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 …

Clustered federated learning is a popular paradigm to tackle data heterogeneity in federated
learning, by training personalized models for groups of users with similar data distributions …

Deep learning has shown incredible potential across a vast array of tasks and
accompanying this growth has been an insatiable appetite for data. However, a large …

While recent years have witnessed the advancement in big data and artificial intelligence, it
is of much importance to safeguard data privacy and security. As an innovative approach …