A Privacy-Preserving Federated Learning Approach for Identifying Offensive Language Online

Core Concepts

A privacy-preserving federated learning approach to train robust models for identifying offensive language online without compromising user privacy.

Abstract

This paper introduces a federated learning (FL) approach to address the challenge of training models for identifying offensive language online while preserving user privacy. The key insights are:

The authors propose a model fusion technique to combine multiple models trained on different datasets using FL, without the need to share the underlying data.

They evaluate the performance of the fused model on four publicly available English offensive language datasets (AHSD, OLID, HASOC, HateXplain) and show that it outperforms both non-fused baselines and ensemble models, while preserving privacy.

The fused model performs best when further fine-tuned on the same dataset used for evaluation, reflecting the ideal scenario where the model needs to perform well on the platform's specific data.

The fused model also generalizes well across different datasets, outperforming non-fused models evaluated on datasets other than the one used for training.

The authors also present initial multilingual experiments on English and Spanish, demonstrating the potential of the FL approach for low-resource languages.

Overall, the paper shows that federated learning is a promising approach for building privacy-preserving models for offensive language identification, outperforming traditional centralized training and ensemble methods.

Stats

"The spread of various forms of offensive speech online is an important concern in social media."
"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."
"Federated Learning (FL) is a decentralized architecture that allows multiple models to be trained locally without the need for data sharing hence preserving users' privacy."

Quotes

"FL is a decentralized architecture that allows multiple models to be trained locally without the need for data sharing hence preserving users' privacy."
"We show that the proposed model fusion approach outperforms baselines in all the datasets while preserving privacy."

Key Insights Distilled From

A Federated Learning Approach to Privacy Preserving Offensive Language Identification

by Marcos Zampi... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11470.pdf

Deeper Inquiries

How can the proposed federated learning approach be extended to other NLP tasks beyond offensive language identification

The proposed federated learning approach can be extended to other NLP tasks beyond offensive language identification by adapting the model fusion technique to suit the specific requirements of different tasks. For tasks such as sentiment analysis, named entity recognition, or text summarization, the same concept of federated learning can be applied. Each participating client can train a model on their local data, and then these models can be fused together to create a more robust and accurate model. By incorporating domain-specific features and fine-tuning the fused model on a specific dataset, the federated learning approach can be tailored to various NLP tasks. Additionally, the use of pre-trained language models like BERT or GPT can further enhance the performance of the federated learning models across different NLP tasks.

What are the potential challenges and limitations of the model fusion technique used in this work, and how can they be addressed

The model fusion technique used in this work may face challenges and limitations related to the diversity of datasets, model architectures, and training strategies. One potential challenge is the compatibility of models trained on different datasets with varying characteristics. Ensuring that the fused model captures the nuances of each dataset while maintaining overall performance can be a complex task. Additionally, the fusion process may introduce biases or inconsistencies if not carefully managed. To address these challenges, it is essential to conduct thorough model evaluations, analyze the impact of dataset variations, and implement techniques like model distillation to reduce model complexity and improve generalization. Regular monitoring and validation of the fused model's performance on diverse datasets can help mitigate potential limitations.

Given the cross-lingual experiments, how can the federated learning framework be further improved to better support low-resource languages and multilingual settings

To better support low-resource languages and multilingual settings in the federated learning framework, several improvements can be implemented. Firstly, incorporating cross-lingual pre-trained models like XLM-R or mBERT can enhance the model's ability to handle multiple languages efficiently. By fine-tuning these models on specific low-resource language datasets within the federated learning framework, the model can learn language-specific features and nuances. Additionally, implementing data augmentation techniques, such as back-translation or synthetic data generation, can help improve the model's performance in low-resource language scenarios. Furthermore, establishing collaborations with language experts and native speakers to provide linguistic insights and annotations can enhance the model's understanding of diverse languages and dialects. Regular updates and adaptations to the federated learning framework based on feedback from multilingual experiments can lead to more robust and effective models for low-resource languages.