Federated learning (FL) is a collaborative, privacy-preserving method for training deep
neural networks at the edge of the Internet of Things (IoT). Despite the many advantages …

Hierarchical federated learning (HFL) has demonstrated promising scalability advantages
over the traditional" star-topology" architecture-based federated learning (FL). However, HFL …

Federated Learning (FL) aims to train high-quality models in collaboration with distributed
clients while not uploading their local data, which attracts increasing attention in both …

Decentralized federated learning (DFL) facilitates collaborative model training across
multiple connected clients without a central coordination server thereby avoiding the single …

Federated learning (FL) training global machine learning models over distributed edge
devices has attracted sustained attentions. However, the heterogeneity of client data …

Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …

Federated Learning (FL), an emerging distributed machine learning (ML), empowers a large
number of embedded devices (eg, phones and cameras) and a server to jointly train a …

Federated learning (FL) is a technique for distributed machine learning (ML), in which edge
devices carry out local model training on their individual datasets. In traditional FL …

Federated Learning (FL) has emerged as a fundamental learning paradigm to harness
massive data scattered at geo-distributed edge devices in a privacy-preserving way. Given …

Federated learning (FL) enables collaborative model training among distributed devices
without data sharing, but existing FL suffers from poor scalability because of global model …