distributed-learning-contributivity
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LabeliaLabs
Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.
Bumps [numpy](https://github.com/numpy/numpy) from 1.19.5 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...
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Bumps [numpy](https://github.com/numpy/numpy) from 1.19.5 to 1.22.0. Release notes Sourced from numpy's releases. v1.22.0 NumPy 1.22.0 Release Notes NumPy 1.22.0 is a big release featuring the work of 153 contributors spread...
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.1 to 2.7.2. Release notes Sourced from tensorflow's releases. TensorFlow 2.7.2 Release 2.7.2 This releases introduces several vulnerability fixes: Fixes a code injection in saved_model_cli (CVE-2022-29216) Fixes...
Bumps [tensorflow](https://github.com/tensorflow/tensorflow) from 2.5.1 to 2.7.2. Release notes Sourced from tensorflow's releases. TensorFlow 2.7.2 Release 2.7.2 This releases introduces several vulnerability fixes: Fixes a code injection in saved_model_cli (CVE-2022-29216) Fixes...
Has anyone considered the possibility of using Pipfile and Pipfile.lock to manage project dependencies instead of the good old requirements.txt file ? this can save us a lot of headaches
This is useful when the number of partners is large
Follow-up of PR #337 , adding a unit test to it.
New contributivity measueament based on statistical distances between 2 distributions: - The partner-specific probability distribution of the label, wrt to the input; (estimated via maximun likelihood wrt to the partner's...
`AIS_Kriging_S` contrib method fails. It now removed from [the end to end tests.](https://github.com/SubstraFoundation/distributed-learning-contributivity/blob/58fa873cb731f8f066bb57917c636f611d4f39e8/tests/contrib_end_to_end_test.py#L66) How to reproduce: scenario = Scenario(2, [0.25, 0.75], epoch_count=4, minibatch_count=2, dataset_name='mnist', contributivity_methods=["AIS_Kriging_S"], dataset_proportion=0.1) exp = Experiment(scenarios_list=[scenario]) exp.run()...
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Simulate collaborative ML scenarios, experiment multi-partner learning approaches and measure respective contributions of different datasets to model performance.