Federated learning (FL) is an exciting new method for securing data privacy in distributed machine learning environments. Quantum computing conditions make it far more difficult to ensure strong privacy protection in FL. Quantum federated learning (QFL) approaches are the focus of this research in order to promote private, confidential, and effective group work. It is proposed that federated learning processes incorporate Quantum Secure Multi-Party Computation (SMPC) protocols. The benefits of QFL in terms of privacy protection and model performance have been empirically demonstrated. QFL’s computational efficiency, scalability, and security are highlighted in thorough performance studies in comparison to conventional FL approaches. The findings highlight the potential for quantum processing to improve the privacy and security of FL, paving the way for decentralized, privacy-preserving quantum-secured collaborative machine learning. In conclusion, empirical evidence supports the efficacy and scalability of QFL, making it a promising option for tackling the difficulties of privacy and security in collaborative machine learning.