In this article, we present federated analytics, a new distributed computing paradigm for data
analytics applications with privacy concerns. With the advances of sensing, communication …

Abstract In Federated Learning, we aim to train models across multiple computing units
(users), while users can only communicate with a common central server, without …

Personalized federated learning is tasked with training machine learning models for multiple
clients, each with its own data distribution. The goal is to train personalized models …

Federated learning (FL) is a decentralized and privacy-preserving machine learning
technique in which a group of clients collaborate with a server to learn a global model …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Differential privacy (DP) is a popular mechanism for training machine learning models with
bounded leakage about the presence of specific points in the training data. The cost of …

Federated learning aims to learn a global model that performs well on client devices with
limited cross-client communication. Personalized federated learning (PFL) further extends …

Federated Learning (FL) is a machine learning setting that separates data and protects user
privacy. Clients learn global models together without data interaction. However, due to the …