distributed-learning-contributivity
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Gaussian noise dataset for corrupted partner
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Today, to simulate a corrupted partner we can set all the labels of its dataset to 1 or shuffle its labels. It could be really interesting if the whole dataset of a corrupted partner can be randomly generated (Features or images, and label)
Interesting indeed. I had not thought about noise on the features... it raise a lot of questions.