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Training tabular generative adversarial networks in a federated learning framework for generating realistic, non-sensitive data for Modatta: a case study for state 0f the art recommendation systems
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Resumo(s)
This work project explores the usefulness of a privacy-preserving framework that
enables Modatta to make personalized recommendations to their users through their application.
For that reason, the Federated Training of Generative Adversarial Networks for Tabular data
was studied, and its performance was evaluated on anonymous data. Choosing the right users
for campaigns has a huge impact on Modatta’s user experience and satisfaction, therefore, state of -the-art Recommendation Systems were investigated.
Descrição
Palavras-chave
Data-privacy Generative adversarial networks Federated learning Systems Tabular data Machine learning Deep learning Recommender systems Collaborative filtering