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.

In this talk I’ll be bringing the audience upto speed with the progress in Privacy preserving machine learning while discussing platforms for developing models and present a demo on healthcare use cases.

 
 

Outline/Structure of the Demonstration

In this talk I’ll be introducing the audience to the emerging field of Privacy preserving machine learning which sits right at the intersection of decentralised machine learning and privacy preserving techniques. The talk will include overview of current platforms along with use case demonstration.

Timewise outline :

00:00- 01:00 : Intro

01:00 - 03:00 :Need for Privacy Aware Machine Learning

03:00 -07:00 : Federated Learning Intro+FL in healthcare

07:00-12:00: Privacy concerns + Tools & Platforms

12:00 -18:00 :Demo

Learning Outcome

This novel technique presents new opportunities for businesses to work around private and sensitive datasets. This demonstration will help audience understand building blocks of developing privacy preserving machine learning.

Target Audience

Machine Learning Engineers, Data Scientists, Product Managers

Prerequisites for Attendees

Knowledge of current machine learning techniques.

schedule Submitted 11 months ago

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    • Bharati Patidar
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