Federated Learning allows edge devices to collaboratively train a shared model on their
local data without leaking user privacy. The non-independent-and-identically-distributed …

Federated learning (FL) can enable distributed model training over mobile nodes without
sharing privacy-sensitive raw data. However, to achieve efficient FL, one significant …

Federated learning of deep neural networks has emerged as an evolving paradigm for
distributed machine learning, gaining widespread attention due to its ability to update …

Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …

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 popular framework for training an AI model using distributed
mobile data in a wireless network. It features data parallelism by distributing the learning …

When applying machine learning techniques to the Internet of things, aggregating massive
amount of data seriously reduce the system efficiency. To tackle this challenge, a distributed …

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm with
enhanced privacy, aiming to achieve a" good" ML model for as many as participants while …

Federated learning (FL) has been widely adopted to train machine learning models over
massive distributed data sources in edge computing. However, the existing FL frameworks …

In the federated learning setting, multiple clients jointly train a model under the coordination
of the central server, while the training data is kept on the client to ensure privacy. Normally …