Federated learning (FL) operates based on model exchanges between the server and the
clients, and suffers from significant communication as well as client-side computation …

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 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 recently made significant progress as a new machine learning
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …

Federated learning (FL) has been widely acknowledged as a promising solution to training
machine learning (ML) model training with privacy preservation. To reduce the traffic …

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 (FL) and split learning (SL) are two popular distributed machine learning
approaches. Both follow a model-to-data scenario; clients train and test machine learning …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

Federated Learning (FL) is a learning paradigm that protects privacy by keeping client data
on edge devices. However, optimizing FL in practice can be difficult due to the diversity and …

In the distributed collaborative machine learning (DCML) paradigm, federated learning (FL)
recently attracted much attention due to its applications in health, finance, and the latest …