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 set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

Federated learning (FL) is an important technique for learning models from decentralized
data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for …

Federated learning (FL) is a decentralized privacy-preserving learning technique in which
clients learn a joint collaborative model through a central aggregator without sharing their …

In recent years, federated learning has attracted more and more attention as it could
collaboratively train a global model without gathering the users' raw data. It has brought …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated Learning (FL) has shown great potential as a privacy-preserving solution to
learning from decentralized data that are only accessible to end devices (ie, clients). The …

Federated learning (FL) allows the collaborative training of a collective model by a vast
number of decentralized clients while ensuring that these clients' data remain private and …

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized
clients to collaboratively learn a common model without sharing local data. Although local …

Traditional machine learning requires collecting data from participants for training, which
may lead to malicious acquisition of privacy in participants' data. Federated learning …