A Federated Approach to Predicting Emojis in Hindi Tweets

TL;DR

This paper introduces a federated learning approach for predicting emojis in Hindi tweets, balancing model accuracy with user privacy, and presents a new dataset and algorithm modification for improved performance.

Contribution

It presents a novel federated learning method, CausalFedGSD, for emoji prediction in Hindi, addressing privacy concerns and reducing data requirements compared to centralized models.

Findings

Achieves comparable accuracy to centralized models

Reduces data needed for model optimization

Minimizes privacy risks in emoji prediction

Abstract

The use of emojis affords a visual modality to, often private, textual communication. The task of predicting emojis however provides a challenge for machine learning as emoji use tends to cluster into the frequently used and the rarely used emojis. Much of the machine learning research on emoji use has focused on high resource languages and has conceptualised the task of predicting emojis around traditional server-side machine learning approaches. However, traditional machine learning approaches for private communication can introduce privacy concerns, as these approaches require all data to be transmitted to a central storage. In this paper, we seek to address the dual concerns of emphasising high resource languages for emoji prediction and risking the privacy of people's data. We introduce a new dataset of $118$k tweets (augmented from $25$k unique tweets) for emoji prediction in Hindi, and propose a modification to the federated learning algorithm, CausalFedGSD, which aims to strike a balance between model performance and user privacy. We show that our approach obtains comparative scores with more complex centralised models while reducing the amount of data required to optimise the models and minimising risks to user privacy.