Federated Learning (FL) is a way for machines to learn from data that is kept locally, in order
to protect the privacy of clients. This is typically done using local SGD, which helps to …

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) provides a privacy-preserving solution for distributed machine
learning tasks. One challenging problem that severely damages the performance of FL …

Federated learning (FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

Federated Learning (FL) enables a large number of data owners (aka FL clients) to jointly
train a machine learning model without disclosing private local data. The importance of local …

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 …

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 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 …

Recently, the ever-growing demand for privacy-oriented machine learning has motivated
researchers to develop federated and decentralized learning techniques, allowing individual …