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 a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …

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 distributed learning approach that is rapidly
developing in an era where privacy protection is increasingly valued. It is this rapid …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) has emerged as a secure paradigm for collaborative training among
clients. Without data centralization, FL allows clients to share local information in a privacy …

Federated Learning (FL) is an approach to machine learning in which the training data are
not managed centrally. Data are retained by data parties that participate in the FL process …

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 …