Federated Learning is a decentralized machine learning approach that enables model training on distributed data sources while maintaining data privacy and security. This is particularly important in NLP applications, where sensitive personal data is often involved, such as in healthcare, finance, and personal communication. The research will explore how Federated Learning can enhance NLP tasks while addressing the challenges of data heterogeneity, communication efficiency, and model accuracy.This research aims to significantly enhance the field of Natural Language Processing by integrating federated learning techniques that prioritize user privacy while improving model performance and adaptability across diverse datasets. Leveraging advanced tools and methodologies, the research will address critical challenges in data security and model accuracy, contributing valuable insights to the future of privacy-preserving machine learning in NLP.