Closed arthurPignet closed 3 years ago
The release note is really cool, well done @arthurPignet!
Fixing a few English typos:
We planned to release a new version of mplc on git, pip and conda.
the release note are shown below, please don't hesitated to comment !
This release introduces several changes to the library which is now offering more modularity. It is also deployed on PyPI.
Experiment
object: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275random
, permutation
, duplication
, redundancy
ways to corrupt a dataset. #280 & #277Dataset
class. #262FederatedGradient
: New multi-partner learning method. The gradients are aggregated (instead of the weights). Then the optimizer updates the model with the aggregated gradient. #299‘local’
, these test/validation datasets are splitted between partners, in the exact same way than the training one. #288This release received successful contributions from:
We planned to release a new version of mplc on git, pip and conda.
the release note are shown below, please don't hesitated to comment !
MPLC v0.3 Release notes
This release introduces several changes to the library which is now offering more modularity. It is also deployed on PyPI.
Features
Experiment
object: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275random
,permutation
,duplication
,redundancy
ways to corrupt a dataset. #280 & #277Dataset
class. #262FederatedGradient
: New multi-partner learning method. The gradients are aggregated (instead of the weights). Then the optimizer updates the model with the aggregated gradient. #299‘local’
, these test/validation datasets are splitted between partners, in the exact same way than the training one. #288Fixes
Contributors
This release received successful contributions from: