Federated learning (FL) provides autonomy and privacy by design to participating peers,
who cooperatively build a machine learning (ML) model while keeping their private data in …

Federated learning (FL) provides autonomy and privacy by design to participating peers,
who cooperatively build a machine learning (ML) model while keeping their private data in …

Federated learning (FL) has recently emerged as a new form of collaborative machine
learning, where a common model can be learned while keeping all the training data on local …

Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of
participants to construct a joint ML model without exposing their private training data …

Federated learning (FL) enables learning a global machine learning model from data
distributed among a set of participating workers. This makes it possible (i) to train more …

Federated learning has migrated data-driven learning to a model-centric approach. As the
server does not have access to the data, the health of the data poses a concern. The …

Federated learning (FL), as a distributed machine learning setting, has received
considerable attention in recent years. To alleviate privacy concerns, FL essentially …

Federated learning has a variety of applications in multiple domains by utilizing private
training data stored on different devices. However, the aggregation process in federated …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Federated Learning (FL) allows multiple clients to collaboratively train a Neural Network
(NN) model on their private data without revealing the data. Recently, several targeted …