Federated learning & Security (Story of a CEO & 2 researchers)

Hello Followers & Data Friends,

Again, thanks to someone who wrote this in my book review and helped me to restart this blog again.. I am glad someone still remembers my old blog and so many got motivated to enter AI & Data Science after reading it. My humble thank you - 

It's Sunday night (11:30 PM) and amazing times that we live in where AI and Data is the new oil. And it's also the time to have a deep look at Sundar's last month's interview where he spoke about Federated Learning (starting at 27:50). (Yeah, it was another busy weekend with lot of work but I still listen to Sundar's speeches on my walk) - https://www.youtube.com/watch?v=EuF8nv53JeI&t=1997s

Federated Learning & Edge Computing is an evolving topic most popular since Google announced its cookie-less marketing world last year - https://www.gartner.com/en/marketing/insights/articles/three-steps-for-marketers-to-prepare-for-a-cookieless-world

Sundar also spoke about Privacy, AI and a lot of futuristic things.

The books/papers that came to my mind immediately after listening to the interview are -

1. Milind Tambe's book on Security & Game Theory (2011) - https://teamcore.seas.harvard.edu/publications/security-and-game-theory-algorithms-deployed-systems-lessons-learned

This is one game theory book I liked a lot in 2011 and wrote about it in my blog that time. It was fresh with good perspective security & game theory while it was written in a simple language. Also, amazing amazing chapter 12 on Stackelberg versus Nash in Security Games. Every game theory & security student should read this chapter and if possible attend Milind Tambe's lectures. His lectures have amazing clarity & awesome vision. (Yes. I don't promote my own books 😜)

2. Advances and Open Problems in Federated Learning (2019) - https://arxiv.org/abs/1912.04977 by Ramesh Raskar & the team. A wonderful wonderful paper on Federated Learning. 

The chapter I Like in this paper/booklet is - Adapting ML Workflows for Federated Learning, Hyperparameter Tuning and Neural Architecture Search (NAS) which is inspired by Chaoyang He, Murali Annavaram, and Salman Avestimehr' s paper on FEDNAS - Federated deep learning via neural architecture search. 

I will try to cover these things in my any upcoming guest lectures very soon. I have been extremely busy. For now, it's time to listen to Milind Tambe's all lectures from the past including AI for Social Good lecture including his JPMorgan lecture... 

More coming soon on this topic...