Federated learning is an emerging technique to collaboratively train machine learning
models over multiple clients without exposing private data but suffers from heterogeneous …

Federated learning (FL) facilitates multiple clients to jointly train a machine learning model
without sharing their private data. However, heterogeneous data that is not independent and …

Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to
train across decentralized clients as practical applications. We present Global-Regularized …

Data heterogeneity is one of the most challenging issues in federated learning, which
motivates a variety of approaches to learn personalized models for participating clients. One …

Personalized federated learning is aimed at allowing numerous clients to train personalized
models while participating in collaborative training in a communication-efficient manner …

Federated learning (FL) is a decentralized machine learning technique that enables multiple
clients to collaboratively train models without requiring clients to reveal their raw data to …

Federated learning aims to collaboratively train models without accessing their client's local
private data. The data may be Non-IID for different clients and thus resulting in poor …

Federated learning is a distributed machine learning approach in which a single server and
multiple clients collaboratively build machine learning models without sharing datasets on …

We present a class of methods for robust, personalized federated learning, called Fed+, that
unifies many federated learning algorithms. The principal advantage of this class of methods …

Federated learning is a distributed machine learning approach that allows a single server to
collaboratively build machine learning models with multiple clients without sharing datasets …