Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

This study develops a federated learning (FL) framework overcoming largely incremental
communication costs due to model sizes in typical frameworks without compromising model …

Federated learning (FL) is an emerging machine learning paradigm in which a central
server coordinates multiple participants (aka FL clients) to train a model collaboratively on …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

Federated learning (FL) is a promising approach for learning a shared global model on
decentralized data owned by multiple clients without exposing their privacy. In real-world …

The burst of applications empowered by massive data have aroused unprecedented privacy
concerns in AI society. Currently, data confidentiality protection has been one core issue …

Federated learning has received increasing attention for its ability to collaborative learning
without leaking privacy. Promising advances have been achieved under the assumption that …