Federated Learning (FL) is a newly emerged federated optimization technique for distributed
data in a federated network. The participants in FL that train the model locally are classified …

Federated learning (FL) is an emerging distributed machine learning (ML) framework that
operates under privacy and communication constraints. To mitigate the data heterogeneity …

Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a
global model under the orchestration of a central server while keeping privacy-sensitive data …

Federated Learning (FL) is a promising decentralized machine learning framework that
enables a massive number of clients (eg, smartphones) to collaboratively train a global …

Federated learning (FL), as a machine learning framework, has garnered substantial
attention from researchers in recent years. FL makes it possible to train a global model …

Federated Learning (FL) has been widely used to facilitate distributed and privacy-
preserving machine learning in recent years. Different from centralized training that usually …

Federated Learning (FL) aims to generate a global shared model via collaborating
decentralized clients with privacy considerations. Unlike standard distributed optimization …

Federated Learning (FL) is a privacy-protected machine learning paradigm that allows
model to be trained directly at the edge without uploading data. One of the biggest …

Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of
machine learning models on networked devices (eg, mobile devices, IoT edge nodes). It …

Federated Learning (FL) is a new distributed machine learning paradigm that breaks down
data barriers and protects data privacy at the same time, thereby enabling clients to …