Federated Learning Meets Natural Language Processing: A Survey
It addresses privacy issues in NLP for users by summarizing existing work, but is incremental as it is a survey.
This survey examines the integration of federated learning with natural language processing to address privacy concerns by learning models from decentralized text data without centralizing it, reviewing challenges, evaluation methods, and future directions.
Federated Learning aims to learn machine learning models from multiple decentralized edge devices (e.g. mobiles) or servers without sacrificing local data privacy. Recent Natural Language Processing techniques rely on deep learning and large pre-trained language models. However, both big deep neural and language models are trained with huge amounts of data which often lies on the server side. Since text data is widely originated from end users, in this work, we look into recent NLP models and techniques which use federated learning as the learning framework. Our survey discusses major challenges in federated natural language processing, including the algorithm challenges, system challenges as well as the privacy issues. We also provide a critical review of the existing Federated NLP evaluation methods and tools. Finally, we highlight the current research gaps and future directions.