A Federated Learning Approach to Privacy Preserving Offensive Language
  Identification
Marcos Zampieri Damith Premasiri Tharindu Ranasinghe
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 for identifying offensive language online
by introducing Federated Learning (FL) in the context of offensive language
identification. FL is a decentralized architecture that allows multiple models
to be trained locally without the need for data sharing hence preserving users'
privacy. We propose a model fusion approach to perform FL. We trained multiple
deep learning models on four publicly available English benchmark datasets
(AHSD, HASOC, HateXplain, OLID) and evaluated their performance in detail. We
also present initial cross-lingual experiments in English and Spanish. We show
that the proposed model fusion approach outperforms baselines in all the
datasets while preserving privacy.