To mitigate the privacy leakages and communication burdens of Federated Learning (FL),
decentralized FL (DFL) discards the central server and each client only communicates with …

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 a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

Federated learning (FL) is a promising paradigm to enable privacy-preserving deep learning
from distributed data. Most previous works are based on federated average (FedAvg), which …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated learning (FL) is an emerging distributed machine learning paradigm that protects
privacy and tackles the problem of isolated data islands. At present, there are two main …

Federated learning (FL) can enable distributed model training over mobile nodes without
sharing privacy-sensitive raw data. However, to achieve efficient FL, one significant …

Federated learning (FL) enables distributed participants to collectively learn a strong global
model without sacrificing their individual data privacy. Mainstream FL approaches require …

Federated learning is an emerging distributed machine learning framework which jointly
trains a global model via a large number of local devices with data privacy protections. Its …

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 …