Due to privacy protection, the conventional machine learning approaches, which upload all
data to a central location, has become less feasible. Federated learning, a privacy …

Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …

The data generated and stored in mobile devices owned by individuals as well as in various
organizations contains a large amount of valuable and important information that can be …

Federated Learning (FL) is a privacy preserving machine learning scheme, where training
happens with data federated across devices and not leaving them to sustain user privacy …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated learning is a decentralized learning approach that shows promise for preserving
users' privacy by avoiding local data sharing. However, the heterogeneous data in federated …

Federated learning is a novel distribute machine learning paradigm to support cooperative
model training among multiple participant clients, where each client keeps its private data …

Federated learning is a distributed machine learning technique that allows several data
owners to jointly train shared models without exchanging their confidentials data as the …

Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

Federated learning (FL) is an important paradigm for training global models from
decentralized data in a privacy-preserving way. Existing FL methods usually assume the …