Federated learning enables multiple participants to collaboratively train a model without
aggregating the training data. Although the training data are kept within each participant and …

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized
clients to collaboratively learn a common model without sharing local data. Although local …

Federated learning (FL) has been widely regarded as a promising paradigm for privacy
preservation of raw data in machine learning. Although, the data privacy in FL is locally …

To preserve participants' privacy, Federated Learning (FL) has been proposed to let
participants collaboratively train a global model by sharing their training gradients instead of …

Federated learning is a large scale machine learning training paradigm where data is
distributed across clients, and can be highly heterogeneous from one client to another. To …

Federated learning (FL) is a distributed learning paradigm that allows multiple decentralized
edge devices to collaboratively learn toward a common objective without sharing local data …

Federated learning is a machine learning setting where a set of edge devices collaboratively
train a model under the orchestration of a central server without sharing their local data. At …

Federated learning (FL) enables distributed agents to collaboratively learn a centralized
model without sharing their raw data with each other. However, data locality does not …

Federated learning (FL) is a distributed machine learning (ML) framework where multiple
clients collaborate to train a model without exposing their private data. FL involves cycles of …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …