Federated learning (FL) is an emerging learning paradigm without violating users' privacy.
However, large model size and frequent model aggregation cause serious communication …

Recently, federated learning (FL) has sparked widespread attention as a promising
decentralized machine learning approach which provides privacy and low delay. However …

Federated learning (FL) is a framework of large-scale distributed learning with user privacy
protection through local training and global aggregation. However, FL may suffer from …

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for
distributed learning. In this work, we mainly focus on the optimization of computation and …

Federated Learning (FL) is an emerging machine learning framework designed to preserve
privacy. However, the continuous updating of model parameters over uplink channels with …

Federated learning (FL) has attracted tremendous attentions in recent years due to its
privacy-preserving measures and great potential in some distributed but privacy-sensitive …

Federated learning (FL) has been recognized as a viable distributed learning paradigm
which trains a machine learning model collaboratively with massive mobile devices in the …

Recently, Federated Learning (FL) has drawn tremendous attentions due to its ability to
protect client's privacy. In FL, clients collaboratively train machine learning models by merely …

Federated learning (FL) protects data privacy through local training and parameter
aggregation. However, there is no need that all users are required to train their local models …

Federated learning (FL) enables distributed clients to collectively train a global model
without revealing their private data, and for efficiency clients synchronize their gradients …