An Automated Framework for Distributed Deep Learning–A Tool Demo

The demo plan:

1. Introduce the motivation: unlocking private data sharing
2. Explain the underlying methodology of our innovations: Blind Learning and Privophy
3. Illustrate the usage of TripleBlind with a live example on training an image classifier using two remote decentralized datasets.
4. Invite the audience to play with our system.

Audience interaction plan:

Members of the audience will be invited to interact with our system. Each participant will play the role of either a data scientist or a data owner.

• The data scientist task will focus on training a deep learning model using remote decentralized datasets
• The data owner task will focus on running an encrypted inference using a remote model on some local data

The aforementioned tasks will be accessible through live Jupyter notebooks. Each notebook will include one of four scenarios (see below). Note that each dataset is divided into two sets, each set is placed on an individual Google Cloud instance to simulate training and inference on decentralized data owned by different organizations.

• Training a deep learning model (VGG-16) for image classification using CIFAR-10
• Training a deep learning model for tabular data classification
• Training a deep learning model for multi-modal (text and images) classification
• Running a secure inference task using a remote, pre-trained model
• We have also added another notebook to illustrate our Private Set Intersection protocol

Note: The Jupyter notebooks server will be kept alive for the reviewers to access and test the notebooks. However, we will reduce the computational capacity for these servers in this period due to their cost. We will improve the computational capacity for the server during the actual demo to accommodate as many participants as possible.