Federated learning is a growing field in the machine learning community due to its decentralized and private design. Model training in federated learning is distributed over multiple clients giving access to lots of client data while maintaining privacy. Then, a server aggregates the training done on these multiple clients without access to their data, which could be emojis widely used in any social media service and instant messaging platforms to express users' sentiments. This paper proposes federated learning-based multilingual emoji prediction in both clean and attack scenarios. Emoji prediction data have been crawled from both Twitter and SemEval emoji datasets. This data is used to train and evaluate different transformer model sizes including a sparsely activated transformer with either the assumption of clean data in all clients or poisoned data via label flipping attack in some clients. Experimental results on these models show that federated learning in either clean or attacked scenarios performs similarly to centralized training in multilingual emoji prediction on seen and unseen languages under different data sources and distributions. Our trained transformers perform better than other techniques on the SemEval emoji dataset in addition to the privacy as well as distributed benefits of federated learning.

翻译：联邦学习因其去中心化和隐私保护的设计，正成为机器学习社区中一个日益发展的领域。联邦学习中的模型训练分布在多个客户端上，在确保隐私的同时可获取大量客户端数据。随后，服务器在不接触客户端数据的情况下聚合这些客户端的训练结果，这些数据可能包括广泛应用于各类社交媒体和即时通讯平台以表达用户情感的emoji表情符号。本文提出基于联邦学习的多语言表情符号预测方法，涵盖清洁场景与攻击场景。我们从Twitter和SemEval表情数据集爬取表情符号预测数据，用于训练和评估不同规模的Transformer模型（包括稀疏激活Transformer），分别假设所有客户端存在清洁数据，或部分客户端通过标签翻转攻击注入污染数据。实验结果表明，在清洁或攻击场景下，联邦学习在跨不同数据源与分布的可见及未见语言的多语言表情符号预测中，性能与集中式训练相近。与SemEval表情数据集上的其他方法相比，我们训练的Transformer不仅具有更优性能，还兼具联邦学习的隐私保护与分布式优势。