A Federated Learning Approach to Privacy Preserving Offensive Language Identification

arXiv:2404.11470v1 Announce Type: new
Abstract: The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to detect offensive language on social media are trained and/or fine-tuned using large amounts of data often stored in centralized servers. Since most social media data originates from end users, we propose a privacy preserving decentralized architecture …

abstract arxiv cs.cl cs.lg federated learning forms identification investing language media platforms privacy privacy preserving question social social media speech type