codecov-commenter
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3 years ago Codecov Report
Merging #345 (40379dc) into master (ecc3ea8) will decrease coverage by
0.38%. The diff coverage is55.31%.
@@ Coverage Diff @@
## master #345 +/- ##
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- Coverage 80.68% 80.30% -0.39%
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Files 15 15
Lines 3045 3092 +47
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+ Hits 2457 2483 +26
- Misses 588 609 +21 | Impacted Files | Coverage Δ | |
|---|---|---|
| mplc/multi_partner_learning/__init__.py | 100.00% <ø> (ø) |
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| mplc/multi_partner_learning/fast_mpl.py | 61.09% <55.31%> (-0.81%) |
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bowni
HeytemBou
arthurPignet
This PR follows the draft 235, which was too far behind master. This is a rebased version.
New mpl methods:
FedGDO stands for Federated Gradient Double Optimization.
This method is inspired from Federated gradient, but with modification on the local computation of the gradient. In this version we use a local optimizer (partner-specific) to do several minimization steps of the local-loss during a minibatch. We use the sum of these weighs-updates as the gradient which is sent to the global optimizer. The global optimizer aggregates these gradients-like which have been sent by the partners, and performs a optimization step with this aggregated gradient. Here three variations of this mpl method are tested.
The reset of the optimizers can be set via a parameter of the mpl methods. In the same way, a global optimizer different from the local ones can be passed via the mpl arguments.
These methods are tested on this notebook -> https://colab.research.google.com/drive/1CcQpWRpLGldj3iNR7v7Hv2brdwBP3z7D?usp=sharing
Please note that as I am currently working with the notebook, it can change, and be not fully readable Notebook access is currently limited to substra.org, but don't hesitate to come to me for access.