The exponential growth of big data and deep learning has increased the data exchange
traffic in society. Machine Learning as a Service,(MLaaS) which leverages deep learning …

Federated learning facilitates the collaborative training of models without the sharing of raw
data. However, recent attacks demonstrate that simply maintaining data locality during …

This paper presents LDP-Fed, a novel federated learning system with a formal privacy
guarantee using local differential privacy (LDP). Existing LDP protocols are developed …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the adoption of …

Federated learning (FL) is an emerging distributed learning paradigm with default client
privacy because clients can keep sensitive data on their devices and only share local …

Federated learning-based automotive navigation has recently received considerable
attention, as it can potentially address the issue of weak global positioning system (GPS) …

A private machine learning algorithm hides as much as possible about its training data while
still preserving accuracy. In this work, we study whether a non-private learning algorithm can …

Recent years have witnessed remarkable successes of machine learning in various
applications. However, machine learning models suffer from a potential risk of leaking …

Many organizations need large amounts of high quality data for their applications, and one
way to acquire such data is to combine datasets from multiple parties. Since these …