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 …

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed
learning with a network of clients (ie, edge devices). However, the data distribution among …

Federated learning (FL) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

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

As a mechanism for devices to update a global model without sharing data, federated
learning bridges the tension between the need for data and respect for privacy. However …

Federated learning (FL) coordinates with numerous heterogeneous devices to
collaboratively train a shared model while preserving user privacy. Despite its multiple …

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 promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

Federated learning (FL) is an emerging, privacy-preserving machine learning paradigm,
drawing tremendous attention in both academia and industry. A unique characteristic of FL …

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to
train models on their data while preserving their privacy. FL algorithms, such as Federated …