Federated learning (FL) is a machine learning paradigm in which distributed local nodes
collaboratively train a central model without sharing individually held private data. Existing …

Federated Learning (FL) is a machine learning paradigm where local nodes collaboratively
train a central model while the training data remains decentralized. Existing FL methods …

Federated learning (FL) enables leveraging distributed private data for model training in a
privacy-preserving way. However, data heterogeneity significantly limits the performance of …

Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed
clients to collaboratively train models with a central server while keeping raw data locally. In …

Federated Learning (FL) is emerging as a popular, promising decentralized learning
framework that enables collaborative training among clients, with no need to share private …

Federated Learning (FL) is a distributed machine learning approach that enables training on
decentralized data while preserving privacy. However, FL systems often involve resource …

Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables
multiple parties to jointly re-train a shared model without sharing their data with any other …

We consider the problem of Federated Learning (FL) where numerous decentralized
computational nodes collaborate with each other to train a centralized machine learning …

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 privacy-constrained decentralized machine learning paradigm
in which clients enable collaborative training without compromising private data. However …