Secure aggregation is a critical component in federated learning (FL), which enables the
server to learn the aggregate model of the users without observing their local models …

Secure model aggregation is a key component of federated learning (FL) that aims at
protecting the privacy of each user's individual model while allowing for their global …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

Privacy-preserving aggregation protocol is an essential building block in privacy-enhanced
federated learning (FL), which enables the server to obtain the sum of users' locally trained …

Recent attacks on federated learning demonstrate that keeping the training data on clients'
devices does not provide sufficient privacy, as the model parameters shared by clients can …

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

Federated learning is a technique that enables distributed clients to collaboratively learn a
shared machine learning model without sharing their training data. This reduces data …

Federated Learning (FL) enables a large number of users to jointly learn a shared machine
learning (ML) model, coordinated by a centralized server, where the data is distributed …

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 …

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 …