Federated learning is a new machine learning framework that allows mutually distrusting
clients to reap the benefits from the joint training model without explicitly disclosing their …

Federated learning has gained extensive interest in recent years owing to its ability to
update model parameters without obtaining raw data from users, which makes it a viable …

Federated learning (FL) was once considered secure for keeping clients' raw data locally
without relaying on a central server. However, the transmitted model weights or gradients …

Decentralized federated learning (DFL) is an emerging privacy-preserving machine learning
framework, where multiple data owners cooperate to train a global model without any …

Federated learning has emerged as a promising approach for collaborative and privacy-
preserving learning. Participants in a federated learning process cooperatively train a model …

Abstract Machine learning models trained on sensitive real-world data promise
improvements to everything from medical screening to disease outbreak discovery. In many …

Federated learning (FL) protects training data in clients by collaboratively training local
machine learning models of clients for a global model, instead of directly feeding the training …

In recent years, the extensive application of machine learning technologies has been
witnessed in various fields. However, in many applications, massive data are distributively …

Federated learning has emerged as a promising solution for big data analytics, which jointly
trains a global model across multiple mobile devices. However, participants' sensitive data …

Multi-party computation (MPC) allows distributed machine learning to be performed in a
privacy-preserving manner so that end-hosts are unaware of the true models on the clients …