A study led by Professor Taewoon Kim and Mr. Tesfahunegn Minwuyelet Mengistu from Pusan National University, South Korea, explores integrating Federated Learning (FL), Reinforcement Learning (RL), and Natural Language Processing (NLP) to address challenges in privacy, adaptability, and efficiency. The study, made available online on June 8, 2026, and to be published in Volume 62 of Computer Science Review on November 1, 2026, presents FL, RL, and NLP as three co-equal, interdependent pillars. It highlights Low-Rank Adaptation (LoRA)-based federated learning for fine-tuning LLMs, reporting up to 100-fold reductions in communication costs and 30–75% reductions in transmitted data and trainable parameters. Studies reviewed show combining language and reinforcement learning improves sample efficiency by 15–25% and human preference scores by 10–30%.