As a decentralized training paradigm, Federated learning (FL) promises data privacy by
exchanging model parameters instead of raw local data. However, it is still impeded by the …

Federated Learning (FL) enables training Artificial Intelligence (AI) models over end devices
without compromising their privacy. As computing tasks are increasingly performed by a …

The emerging paradigm of federated learning (FL) strives to enable devices to cooperatively
train models without exposing their raw data. In most cases, the data across devices are non …

Federated Learning (FL) is a distributed machine learning system designed to effectively
address potential data privacy concerns, making it particularly promising for the Internet of …

Remote monitoring systems analyze the environment dynamics in different smart industrial
applications, such as occupational health and safety, and environmental monitoring …

Federated learning (FL) emerges to mitigate the privacy concerns in machine learning-
based services and applications, and personalized federated learning (PFL) evolves to …

Clustered federated learning (CFL) has proved to be an effective way to alleviate the non-IID
(not independently and identically distributed) data challenge, which severely restricts the …

Federated Learning is a training framework that enables multiple participants to
collaboratively train a shared model while preserving data privacy. The heterogeneity of …

Federated machine learning (Fed ML) is a new distributed machine learning technique
using clients' local data applied to collaboratively train a global model without transmitting …

With the growth of model/dataset/system size for distributed model training in datacenters,
the widely used Parameter Server (PS) architecture suffers from communication bottleneck …