Episode 3

I talk about how to get privacy to improve the UX through federated learning.

• Google announcement for Federated learning
• Apple announces privacy for their AI models using Differential Privacy

Music: The Pirate And The Dancer by Rolemusic

Transcripts

Hello and welcome to Design for AI
Im Mark Bailey, Welcome to episode 3

Today we will be talking about federated learning.
There is a good chance some of you are wondering what it means,
don’t worry it’s still considered a pretty new topic in AI.
Even the word isn’t pinned down, Apple calls it ‘Differential Privacy’.
so I’ll jump right in to explaining what it is and why it’s important to UX.

The old way, or I guess I should say the normal current way,
most models store data used for machine learning
is to round up all the data you think you’re going to need + data attached to it
then all gets uploaded and stored on your servers.
This is the centralized model
There is the saying going around that data is the new oil,
because the more data you can get your hands on
then the better the accuracy is for your model.
Which means you’re at the front of the line for the gold rush,
right?...

Well, not so fast
There are problems
Some people refer to data as the new plutonium, instead of the new oil
There is a high liability for personal data
Releasing an app over the internet is global.
But, laws and regulations change by country.
The new EU privacy laws like the GDPR conflict with the laws in authoritarian countries where they want you to share all your data.
In steps the idea of federated learning
As a quick side note, I am using Google’s term federated learning,
instead of Apple’s term Differential Privacy.
Differential Privacy is a little more inclusive of making things outside of machine learning models private,
so in the interest of keeping things as specific as possible I’ll use the term federated learning
to keep things as specific as possible.
I’ve included links for both Apple and Google’s announcements in the show notes.

Anyway, it is easiest to think of it in terms of using a cell phone,
because that is where all of this got its start for both companies
On device storage is small and there is too much data to upload over a slow network
The phone downloads the current AI model.
Then it improves the model by learning from all the local data on your phone.
Your phone then summarizes the changes as a small update.
Only this small update is sent back instead of all the data.
For a non-phone example think of Tesla building their self driving cars.
Every car that Tesla is currently making records 8 different cameras every time that car is driving.
Those video feeds help to train the model Tesla is trying to create for the car to drive itself.
To date Tesla has sold over 575,000 cars since 2014 when they added the cameras needed for self driving.
multiple 575,000 by 8 then multiply that by the number of miles all those cars drive.
It becomes obvious that is just too many video feeds to send over their wireless network
much less to record and store on central servers somewhere.
More importantly, no one wants everywhere they have driven,
and every mistake they made to come back to haunt them.
federated learning allows Tesla to push the model out to their cars.
Let the model be trained by data collected in the car,
then the training corrections are sent back to Tesla without needing to send hours upon hours of video.
Privacy and data bandwidth are preserved.
As a side note, Tesla does upload some video of a car’s driving for things like accidents.
We talk about outliers and making which parts you keep private later.

So, federated learning allows for global results from local data.
Basically train on the local device and send aggregated results back
It allows to keep the sensitive data on device
and if you can promise, and deliver, privacy to the user of an AI model
then you have taken care of one of the biggest fears users have for machine learning.
Think about it, keeping my data private is one of the biggest complaints against people wanting to use AI.
It is right up there with robots taking over the world,
If we can solve real fears now, we can start working on the science fiction fears next.
This is why it is important to UX
All the benefits of privacy for your customers,
plus all the benefits for the company of well trained models.
Of course offering privacy t...