With data increasingly collected by end devices and the number of devices is growing
rapidly in which data source mainly located outside the cloud today. To guarantee data …

Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural
Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous …

Federated learning (FL) enables edge-devices to collaboratively learn a model without
disclosing their private data to a central aggregating server. Most existing FL algorithms …

Federated learning (FL) is an emerging privacy-preserving paradigm that enables multiple
participants collaboratively to train a global model without uploading raw data. Considering …

Nowadays, there is an ever-increasing deployment of intelligent edge devices, such as
smartphones, wearable devices, and autonomous vehicles. It is enabled by the integration …

Federated Learning (FL) has emerged as a promising technique for edge devices to
collaboratively learn a shared prediction model, while keeping their training data on the …

With the proliferation of Internet of Things (IoT), zillions of bytes of data are generated at the
network edge, incurring an urgent need to push the frontiers of artificial intelligence (AI) to …

Despite achieving remarkable performance, Federated Learning (FL) suffers from two critical
challenges, ie, limited computational resources and low training efficiency. In this paper, we …

Federated learning (FL) enables workers to learn a model collaboratively by using their local
data, with the help of a parameter server (PS) for global model aggregation. The high …

Federated learning can achieve distributed machine learning without sharing privacy and
sensitive data of end devices. However, high concurrent access to cloud servers increases …