Federated learning for natural language processing (NLP) is an emerging research area that enables decentralized, privacy-preserving training of language models across distributed clients, such as mobile devices or organizations, without sharing raw textual data. This paradigm addresses privacy concerns, regulatory constraints, and data heterogeneity while allowing collaborative learning for NLP tasks such as next-word prediction, sentiment analysis, text classification, machine translation, and question answering. Research explores optimization strategies for non-i.i.d. and unbalanced textual data, communication-efficient model updates, personalization techniques, and integration with deep learning architectures including RNNs, LSTMs, and transformer-based models like BERT and GPT. Recent studies also focus on privacy-preserving mechanisms such as differential privacy, secure aggregation, and federated knowledge distillation, as well as robustness against adversarial attacks and domain adaptation challenges. Federated learning for NLP establishes a framework for building scalable, collaborative, and privacy-aware language understanding systems across diverse clients.