Federated Learning (FL) is a prominent framework that enables training a centralized model
while securing user privacy by fusing local, decentralized models. In this setting, one major …

Federated Learning (FL) refers to the decentralized and privacy-preserving machine
learning framework in which multiple clients collaborate (with the help of a central server) to …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Although federated learning (FL) has recently been proposed for efficient distributed training
and data privacy protection, it still encounters many obstacles. One of these is the naturally …

Federated learning (FL) aims to collaboratively train the global model in a distributed
manner by sharing the model parameters from local clients to a central server, thereby …

Federated learning (FL) allows edge devices to collectively learn a model without directly
sharing data within each device, thus preserving privacy and eliminating the need to store …

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 propose a new optimization formulation for training federated learning models. The
standard formulation has the form of an empirical risk minimization problem constructed to …

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 …