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Decentralized and Personalized AI, with Privacy by Design

Decentralized and Personalized AI, with Privacy by Design

This session was presented at the MozFest19, London.

This interactive session will seek answers to the questions, “Why Decentralised Artificial Intelligence Will Reinvent The Industry?” & “What are the risks, promises and foundational blocks of secure and private AI?” It will introduce them to the new dawn of Federated Learning having the potential to disrupt Cloud Computing, the dominant computing paradigm of today.

The session is divided into two phases:
1. Learning Phase: By story-boarding, participants would understand the concept and benefits as well as use cases of decentralized AI. There would be quizzes throughout the session and I would follow the andragogy- Learn. Apply. Share. Repeat.
2. Practicing Phase- The Ideathon: Attendees will get into groups to come up with potential solutions under which problems can fall. They would come up as a team to build an idea by brainstorming and prototyping algorithms and presenting it to the rest in the end with one-on-one mentoring by myself.

Charmi Chokshi

October 27, 2019
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Transcript

  1. Session plan: • Decentralized Data • Federated Computation ◦ Privacy

    principles • Federated Learning ◦ Privacy technologies • Ideathon…!!!
  2. 64 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  3. 65 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  4. 66 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  5. 67 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  6. 68 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  7. 69 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  8. 70 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  9. 71 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  10. 72 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  11. 73 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  12. 74 Data acquisition Model Deployment Data Cleaning Feature Engineering Model

    Validation Model Monitoring Model Selection Model Testing Model Training Hyper parameter tuning
  13. Brainstorm How could federated learning be applied to the ways

    you use a computer* each day? * Laptop, phone, tablet, etc.
  14. References • Federated Optimization: Distributed Optimization Beyond the Datacenter, Jakub

    Konecny, H. Brendan McMahan, Daniel Ramage, 2015 • Decentralized Collaborative Learning of Personalized Models over Networks Paul Vanhaesebrouck, Aurélien Bellet, Marc Tommasi, 2017 • Towards federated learning at scale: system design, Keith Bonawitz Hubert Eichner and al., 2019 • https://federated.withgoogle.com/ • https://www.youtube.com/watch?v=89BGjQYA0uE