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 …

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 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 …

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

Federated Learning (FL) allows training machine learning models in privacy-constrained
scenarios by enabling the cooperation of edge devices without requiring local data sharing …

In federated learning (FL), a cluster of local clients are chaired under the coordination of the
global server and cooperatively train one model with privacy protection. Due to the multiple …

Federated Learning (FL) is transforming the ML training ecosystem from a centralized over-
the-cloud setting to distributed training over edge devices in order to strengthen data …

Federated learning (FL) operates based on model exchanges between the server and the
clients, and suffers from significant communication as well as client-side computation …