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

Federated Learning (FL) enables training a global model without sharing the decentralized
raw data stored on multiple devices to protect data privacy. Due to the diverse capacity of the …

Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated learning promises an elegant solution for learning global models across
distributed and privacy-protected datasets. However, challenges related to skewed data …

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 …

Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …

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) facilitates collaboration between a group of clients who seek to train
a common machine learning model without directly sharing their local data. Although there …

Federated learning is an emerging distributed machine learning framework which jointly
trains a global model via a large number of local devices with data privacy protections. Its …