Federated Learning (FL) aims to improve machine learning privacy by allowing several data
owners in edge and ubiquitous computing systems to collaboratively train a model, while …

Deep learning has shown incredible potential across a vast array of tasks and
accompanying this growth has been an insatiable appetite for data. However, a large …

Federated learning (FL) provides a variety of privacy advantages by allowing clients to
collaboratively train a model without sharing their private data. However, recent studies have …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

Federated Learning (FL) allows several data owners to train a joint model without sharing
their training data. Such a paradigm is useful for better privacy in many ubiquitous …

Federated learning (FL) enables multiple clients to collaboratively train a model with the
coordination of a central server. Although FL improves data privacy via keeping each client's …

Federated Learning (FL) allows multiple participating clients to train machine learning
models collaboratively by keeping their datasets local and only exchanging model updates …

Federated learning (FL) is a distributed, privacy-preserving learning paradigm where a joint
model is trained on private data stored on client devices. Data owners (clients) train models …

Federated learning (FL) has emerged as a promising privacy-preserving machine learning
paradigm, enabling data owners to collaboratively train a joint model by sharing model …

Federated learning (FL) has been widely studied recently due to its property to
collaboratively train data from different devices without sharing the raw data. Nevertheless …