Federated learning-based natural language processing: a systematic literature review
Abstract
Abstract Federated learning (FL) is a decentralized machine learning (ML) framework that allows models to be trained without sharing the participants’ local data. FL thus preserves privacy better than centralized machine learning. Since textual data (such as clinical records, posts in social networks, or search queries) often contain personal information, many natural language processing (NLP) tasks dealing with such data have shifted from the centralized to the FL setting. However, FL is not free from issues, including convergence and security vulnerabilities (due to unreliable or poisoned data introduced into the model), communication and computation bottlenecks, and even privacy attacks orchestrated by honest-but-curious servers. In this paper, we present a systematic literature review (SLR) of NLP applications in FL with a special focus on FL issues and the solutions proposed so far. Our review surveys 36 recent papers published in relevant venues, which are systematically analyzed and compared from multiple perspectives. As a result of the survey, we also identify the most outstanding challenges in the area.
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