Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied. The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81% improvement in terms of F1-score.

翻译：由于社交媒体内容的传播规模之大,仇恨言论的发现一直是引起高度研究关注的主题。尽管任务的关注和敏感性之大,但仇恨言论检测中的隐私保护问题仍然研究不足。大部分研究侧重于有可能泄漏数据的集中式机器学习基础设施。在本文中,我们显示,使用联合式机器学习可以帮助解决与仇恨言论检测相关的隐私问题,同时在F1分数方面获得6.81%的改善。