This paper introduces a new Quantum Federated Learning (QFL) system that combines quantum-enhanced transformers and federated learning to make it possible to perform real-time consumer sentiment analysis securely, at scale, and efficiently. The hybrid model proposed overcomes three significant issues: it can be accurate when working with non-IID data, it is communication-efficient, and it can protect data privacy with high security provided by quantum cryptographic protocols. Experimental analyses on IMDB, Sentiment140, Amazon, Yelp, and our own proprietary data showed that QFL is always better in precision, recall, F1 score, and convergence rate than traditional FL-BERT and FL-LSTM models. Further, QFL was able to demonstrate substantial cost and privacy leakage savings in communication at a low cost as well as maintain scalability to one thousand clients. The study fills the gap between concepts in quantum computing and federated learning and provides a practical route to privacy-sensitive distributed intelligence on a practical scale applied to e-commerce and social media analytics.
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