bowni
commented
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 !
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
- Multiple code refactorization: Refacto the multi-partner learning approaches in a more object oriented way. #255
- New
Experimentobject: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275 - New corruption methods and options: Refacto the corruption in a more object oriented way. Add
random,permutation,duplication,redundancyways to corrupt a dataset. #280 & #277 - Updated datasets integration: Change module architecture. The datasets are now a subclass of the
Datasetclass. #262 - Updated tutorial notebooks. #266
- Documentation: Update and format documentation. #264 & #294
- Add code coverage badge: A new badge on the README, to follow the extension of the test coverage. #268
- More tests added: There is now end-to-end tests for contributivity methods and multi-partner learning approaches. #304 & #300
- Add
FederatedGradient: New multi-partner learning method. The gradients are aggregated (instead of the weights). Then the optimizer updates the model with the aggregated gradient. #299 - Add S-model: New multi-partner learning method. It adds for each partner a NoiseAdaptative layer, which is supposed to adapt to potential label flip and thus improve the shared model performances, even with corrupted partners. #281 & #301
- Add local/global option for test and validation datasets. The dataset is split (only once! and in a stratified way) between train/test/validation datasets. If
‘local’, these test/validation datasets are splitted between partners, in the exact same way than the training one. #288
Fixes
- Fix bugs in corruption by permutation: A transposition was missing. #284
- Update Cifar10 model optimizer: The learning rate was way too low. We also switched from RMSprop to Adam for the optimizer, as it showed better performances. #283
- Fix early stopping: Condition for early stopping was not reachable. #279
- Logs: Add epoch number when showing evaluation metrics. #274
- Dependencies updates: Removal of Keras (We now exclusively use the tensorflow's built-in Keras module), upgrade to Tensor Flow v2.4.0, Numpy v1.19.4, Scikit Learn v0.23.2, Pandas v1.1.5. #290
- Switch to Github Actions instead of Travis for Continuous Integration. #298
- Normalize contributivity scores in step-by-step methods. #295
Contributors
This 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
Experimentobject: An object which runs and repeats several scenarios and gathers results to simplify their analysis. #275random,permutation,duplication,redundancyways to corrupt a dataset. #280 & #277Datasetclass. #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: