Federated learning (FL) is a promising decentralized machine learning approach that
enables multiple distributed clients to train a model jointly while keeping their data private …

Federated learning is a distributed, privacy-aware learning scenario which trains a single
model on data belonging to several clients. Each client trains a local model on its data and …

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 …

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 …

Due to the often limited communication bandwidth of edge devices, most existing federated
learning (FL) methods randomly select only a subset of devices to participate in training at …

Federated Learning (FL) is a prominent framework that enables training a centralized model
while securing user privacy by fusing local, decentralized models. In this setting, one major …

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …

Federated Learning (FL) has gained prominence as a method for training models on edge
computing devices, enabling the preservation of data privacy by eliminating the need to …

Federated Learning (FL) allows machine learning models to train locally on individual
mobile devices, synchronizing model updates via a shared server. This approach …

Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models,
and non-iid labels scenarios. Existing FL methods fail to handle the above three constraints …