In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

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

Federated Learning (FL) has become an established technique to facilitate privacy-
preserving collaborative training. However, new approaches to FL often discuss their …

Federated learning (FL) has attracted vivid attention as a privacy-preserving distributed
learning framework. In this work, we focus on cross-silo FL, where clients become the model …

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 …

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 …

Vertical federated learning (VFL) revolutionizes privacy-preserved collaboration for small
businesses that have distinct but complementary feature sets. However, as the scope of VFL …

Federated learning (FL) is a machine learning paradigm for data silos and privacy
protection, which aims to organize multiple clients for training global machine learning …

Federated learning (FL) enables distributed optimization of machine learning models while
protecting privacy by independently training local models on each client and then …

Federated learning enables the creation of a powerful centralized model without
compromising data privacy of multiple participants. While successful, it does not incorporate …