The burst of applications empowered by massive data have aroused unprecedented privacy
concerns in AI society. Currently, data confidentiality protection has been one core issue …

Federated learning (FL) has emerged as a new paradigm for privacy-preserving
computation in recent years. Unfortunately, FL faces two critical challenges that hinder its …

Federated learning (FL) has attracted growing interest for enabling privacy-preserving
machine learning on data stored at multiple users while avoiding moving the data off-device …

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 …

With an increasing number of smart devices like internet of things devices deployed in the
field, offloading training of neural networks (NNs) to a central server becomes more and …

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed
learning with a network of clients (ie, edge devices). However, the data distribution among …

We consider the problem of Federated Learning (FL) where numerous decentralized
computational nodes collaborate with each other to train a centralized machine learning …

Federated learning (FL) aims to protect data privacy by enabling clients to build machine
learning models collaboratively without sharing their private data. Recent works …

The emerging paradigm of federated learning (FL) strives to enable collaborative training of
deep models on the network edge without centrally aggregating raw data and hence …

State-of-the-art edge devices are capable of not only inferring machine learning (ML)
models but also training them on the device with local data. When this local data is sensitive …