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 …

Federated learning (FL) is a promising approach that enables distributed clients to
collaboratively train a global model while preserving their data privacy. However, FL often …

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 collaborative training of machine learning models on
decentralized data while preserving data privacy. However, data across clients often differs …

Federated learning (FL) is an emerging distributed machine learning paradigm that avoids
data sharing among training nodes so as to protect data privacy. Under the coordination of …

Federated Learning (FL) is an emerging research field that yields a global trained model
from different local clients without violating data privacy. Existing FL techniques often ignore …

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) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

Federated Learning (FL) offers a collaborative training framework, allowing multiple clients
to contribute to a shared model without compromising data privacy. Due to the …

Federated learning (FL) typically faces data heterogeneity, ie, distribution shifting among
clients. Sharing clients' information has shown great potentiality in mitigating data …