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[WIP] Add Federated Learning and Differential Privacy demos #1566

merged 9 commits into from Nov 2, 2018


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@mukul-rathi mukul-rathi commented Sep 26, 2018


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:


  • 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

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Contributor Author

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.

@mukul-rathi mukul-rathi changed the title Add Initial Fed/Diff Boston Demo Add Federated Learning and Differential Privacy demos Sep 26, 2018
@mukul-rathi mukul-rathi changed the title Add Federated Learning and Differential Privacy demos [WIP] Add Federated Learning and Differential Privacy demos Sep 26, 2018
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SUPER excited to see this coming together!!!

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mukul-rathi commented Oct 8, 2018

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?

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

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

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iamtrask commented Nov 2, 2018

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

@iamtrask iamtrask merged commit 2c7218a into OpenMined:master Nov 2, 2018
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3 participants