Numerous research recently proposed integrating Federated Learning (FL) to address the
privacy concerns of using machine learning in privacy-sensitive firms. However, the …

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Evaluation is a systematic approach to assessing how well a system achieves its intended
purpose. Federated learning (FL) is a novel paradigm for privacy-preserving machine …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated Learning (FL) has emerged as a promising approach for distributed machine
learning. FL enables multiple parties to collaboratively train a model without sharing their …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

The past four years have witnessed the rapid development of federated learning (FL).
However, new privacy concerns have also emerged during the aggregation of the …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …