[WIP] Add Federated Learning and Differential Privacy demos

[mukul-rathi]

Description

Adding demos combining Differential Privacy and Federated Learning

Fixes #1556

Type of change

• New feature (non-breaking change which adds functionality)
• This change requires a documentation update

How Has This Been Tested?

Not yet (WIP)

• Test A
• Test B

Test Configuration:

• CPU:
• GPU:
• PySyft Version:
• Unity Version:
• OpenMined Unity App Version:

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published in downstream modules

[mukul-rathi]

This is the WIP PR - so far I've taken the code from the Boston Housing Dataset Federated learning and Differential Privacy demos and put them into a single IPython Notebook. There is still some code that needs to be combined in the train method.

[iamtrask]

SUPER excited to see this coming together!!!

[mukul-rathi]

I think there may be a bug in model.send(worker)? Since there is a key error in workers._objects() when I run the forward prop step pred=model(data).

However, when I send the parameters of the model individually using param.send(worker) their ids are stored in workers._objects().keys() correctly so there is no key error in workers._objects() when I run the forward prop.

There is a key error in workers._objects() when the program then calls optimizer.zero_grad() on the same worker when the next lot is iterated through. Is there a corresponding optimizer.send() operation that I should be sending the worker?

[iamtrask]

Hey @mukul-rathi - try master now. i think there might have been a bug in model.send() for a minute.

[mukul-rathi]

@iamtrask thanks a lot! The notebook works now - can you look over it?
I'll get on with the second notebook now!

[iamtrask]

Excellent work! Merging your first notebook now! (we can merged the second later) :)