Early Detection of Sexual Predators with Federated Learning

Khaoula Chehbouni, Gilles Caporossi, Reihaneh Rabbany, Martine De Cock, Golnoosh Farnadi

Keywords: federated learning, differential privacy, language model, text classification, online grooming

TL;DR: A privacy-preserving framework for the early detection of sexual predators leveraging federated learning and differential privacy

Abstract: The rise in screen time and the isolation brought by the different containment measures implemented during the COVID-19 pandemic have led to an alarming increase in cases of online grooming. Online grooming is defined as all the strategies used by predators to lure children into sexual exploitation. Previous attempts made in industry and academia on the detection of grooming rely on accessing and monitoring users’ private conversations through the training of a model centrally or by sending personal conversations to a global server. We introduce a first, privacy-preserving, cross-device, federated learning framework for the early detection of sexual predators, which aims to ensure a safe online environment for children while respecting their privacy.

Is Student: Yes