Federated Quantum Neural Networks with Fully Homomorphic Encryption: A Privacy-Preserving Approach to Distributed Machine Learning

Federated Learning with Quantum Neural Networks and Fully Homomorphic Encryption provides a novel computing paradigm shift for privacy-preserving machine learning, addressing challenges in communication efficiency, data privacy, and computational overhead.