Privacy preserving machine learning is an emerging field which is in active research. The most prolific successful machine learning models today are built by aggregating all data together at a central location. While centralised techniques are great , there are plenty of scenarios such as user privacy, legal concerns ,business competitiveness or bandwidth limitations ,wherein data cannot be aggregated together. Federated Learningcan help overcome all these challenges with its decentralised strategy for building machine learning models. Paired with privacy preserving techniques such as encryption and differential privacy, Federated Learning presents a promising new way for advancing machine learning solutions.

1. Privacy Preserving Machine
Learning Techniques
Amogh Tarcar
Machine Learning Researcher

2. Index
• Need for Privacy Aware Machine Learning
• Federated Learning Intro
• FL in healthcare
• Privacy concerns
• Tools & Platforms
• Demo

3. Need for Privacy Aware Machine Learning
• Data sources such as EHR , X-Rays, Genomics data are inherently
sensitive and private, and there are ethical as well as legal limitations
for aggregating sensitive data
• Healthcare datasets , especially for rare diseases needs collaboration
of health providers across the world
• Privacy aware machine learning technique enables building models
without needing data to move from its source location. One such
technique is called Federated Learning.

4. Federated
Learning
Source : https://blog.fastforwardlabs.com/images/editor_uploads/2018-10-31-181344-federated_learning_animated_labeled.gif

5. Federated
Machine
Learning for
Healthcare
Source: https://blogs.nvidia.com/wp-content/uploads/2019/10/federated_learning_animation_still_white.png

6. Privacy Concerns
• Federated learning is a variant of
decentralized machine learning
• Even though data does not leave the
source location, the model parameters
may leak info about the data
• Federated learning needs to be
implemented using secure techniques
such as :
• Differential Privacy / Secure
Aggregation
• Secure Multi Party Computation /
Encryption
Image Source : https://www.google.com/url?sa=i&url=http%3A%2F%2Fblog.fastforwardlabs.com%2F2018%2F11%2F14%2Ffederated-
learning.html&psig=AOvVaw0tpZYKhKDMX3h5MOCEXkNC&ust=1585489636330000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCLiGuqmnvegCFQAAAAAdAAAAABAD

7. Tools & Platforms
TensorFlow FederatedPySyft
NVIDIA CLARA
FedAI
OWKIN Platform

8. Healthcare Use case Demo