Federated Learning enables mobile users to collaboratively learn a global prediction model
by aggregating their individual updates without sharing the privacy-sensitive data. As mobile …

Federated learning enables distributed model training over various computing nodes, eg,
mobile devices, where instead of sharing raw user data, computing nodes can solely commit …

FedLab is a lightweight open-source framework for the simulation of federated learning. The
design of FedLab focuses on federated learning algorithm effectiveness and communication …

Federated learning allows edge devices to collaboratively train a global model by
synchronizing their local updates without sharing private data. Yet, with limited network …

Federated learning (FL) enables on-device training over distributed networks consisting of a
massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) …

Federated learning enables distributed learning in a privacy-protected manner, but two
challenging reasons can affect learning performance significantly. First, mobile users are not …

Federated learning (FL) has attracted increasing attention as a promising approach to
driving a vast number of end devices with artificial intelligence. However, it is very …

The widespread deployment of machine learning applications in ubiquitous environments
has sparked interests in exploiting the vast amount of data stored on mobile devices. To …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

Federated learning (FL) is becoming a popular paradigm for collaborative learning over
distributed, private data sets owned by nontrusting entities. FL has seen successful …