Advancing Quantum Federated Learning for Enhanced Privacy in Collaborative Machine Learning Environments

This study examines quantum-secured collaborative machine learning, focusing on quantum federated learning (QFL) protocols. Quantum physics is used to improve collaborative machine learning model security and privacy. This research uses quantum computing to improve federated learning and mitigate traditional shortcomings. Quantum entanglement and superposition provide new cryptographic primitives and secure multi-party computations. This method protects sensitive data and reduces risks during collaborative model training. The quantum-secured collaborative machine learning framework is also empirically evaluated in several areas to determine its practicality. The tests show that the technique is robust against hostile threats and accurate in the model. Quantum computing ideas are integrated into this research to advance privacy-preserving collaborative machine learning. The article proposes quantum federated learning to address data privacy problems in advanced machine learning applications. This paradigm seeks safe and effective collaborative model training.