Privacy-Preserving Federated Learning for Hate Speech Detection

MCML Authors

Haotian Ye

→ Group Hinrich Schütze
 Computational Linguistics

Axel Wisiorek

Dr.

→ Group Hinrich Schütze
 Computational Linguistics

Hinrich Schütze

Prof. Dr.

Core PI

Computational Linguistics

Abstract

This paper presents a federated learning system with differential privacy for hate speech detection, tailored to low-resource languages. By fine-tuning pre-trained language models, ALBERT emerged as the most effective option for balancing performance and privacy. Experiments demonstrated that federated learning with differential privacy performs adequately in low-resource settings, though datasets with fewer than 20 sentences per client struggled due to excessive noise. Balanced datasets and augmenting hateful data with non-hateful examples proved critical for improving model utility. These findings offer a scalable and privacy-conscious framework for integrating hate speech detection into social media platforms and browsers, safeguarding user privacy while addressing online harm.

inproceedings BIY+25

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SRW @NAACL 2025

Student Research Workshop at the Annual Conference of the North American Chapter of the Association for Computational Linguistics. Albuquerque, NM, USA, Apr 29-May 04, 2025.

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Authors

I. d. S. Bueno Júnior • H. Ye • A. Wisiorek • H. Schütze

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Links

DOI

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Research Area

 B2 | Natural Language Processing