Mitigating Online Grooming with Federated Learning

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 on the detection of grooming in the industry and academia 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. Empirical evaluation on a real-world dataset indicates that the performance of our framework is as good as the performance of a centrally trained model.