The real-world implementation of federated learning is complex and requi...
Federated learning (FL) is an effective solution to train machine learni...
The aim of Machine Unlearning (MU) is to provide theoretical guarantees ...
We propose a novel framework to study asynchronous federated learning
op...
Federated learning allows clients to collaboratively learn statistical m...
The increasing size of data generated by smartphones and IoT devices
mot...
While clients' sampling is a central operation of current state-of-the-a...
This work addresses the problem of optimizing communications between ser...
Federated learning usually employs a client-server architecture where an...
Free-rider attacks on federated learning consist in dissimulating
partic...