Quantum-enhanced federated learning (QFL) represents a cutting-edge paradigm that integrates quantum computing and quantum communication technologies with federated learning to address emerging challenges in distributed artificial intelligence. This chapter presents a comprehensive framework combining variational quantum circuits, quantum-safe cryptographic protocols, and privacy-preserving aggregation mechanisms to improve model accuracy, enhance communication efficiency, and accelerate convergence speed. Extensive experiments on both classical and quantum datasets demonstrate QFL’s superiority over classical federated and centralized quantum models, achieving up to 50% reduction in communication overhead and faster training convergence. Security analyses confirm robust privacy guarantees against classical and quantum adversaries through quantum key distribution and post-quantum secret sharing. The methodology addresses practical challenges such as hardware heterogeneity, non-IID data, and network scaling using adaptive model personalization, hierarchical aggregation, and compressed parameter transmission. Nonetheless, limitations related to noisy intermediate-scale quantum (NISQ) devices, hardware scalability, and protocol complexity persist. This discussion highlights the current state and future directions of QFL, emphasizing the necessity of interdisciplinary advances in quantum engineering, machine learning, and cryptography for realizing secure, scalable, and efficient distributed AI in the quantum era.