Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple
participants collaboratively to train a global model without uploading raw data. Considering …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Federated learning (FL) is an emerging distributed and privacy-preserving machine learning
framework. However, the performance of traditional FL methods is seriously impaired by the …

Federated learning promises an elegant solution for learning global models across
distributed and privacy-protected datasets. However, challenges related to skewed data …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …

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) 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) is a distributed learning technique that trains a shared model over
distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades …

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 …