ederated learning allows collaborative model training across de centralized devices or organizations without sharing private data, an emerging approach that addresses critical data privacy issues in machine learning but also introduces novel challenges com pared to traditional centralized methods. On the other hand, Quantum computing is integrated in Quan tum federated learning to further enhance privacy, security, and efficiency, while also being a factor of weakness if used by attack ers considering the enormous capacity of processing that could give them the upper hand in their conducted attacks Ongoing research are exploring decentralized block-chain-based frameworks, asynchronous training, post-quantum cryptography for security and many other techniques to prevent such leverage, while key research directions include tackling statistical hetero geneity across decentralized data, ensuring robustness to unre liable clients, reducing communication costs, defending against inference attacks, and securing privacy. Real-world applications of Quantum-inspired federated learning includes healthcare, finance, connected vehicles and beyond. This article will address the following: The characteristics and challenges of this promising evolution of machine learning known as federated learning that promotes decentralized collaboration while preserving data privacy • Real-life applications during quantum-computing era • Continued research needed to address the open challenges re volved around security, efficiency, and governance • Provide our own proposition to deploy best-in-class framework in order to defend against inference attacks throughout the ap plication of the following: 1. Introduce novel security measures for the aggregation server, blending Quantum Differential Privacy and Homomorphic Encryption 2. Design a decentralized architecture for server-client commu nications, enhancing communication efficiency. 3. Develop advanced security measures for Problem Solving Nodes and Random Operational Feedback, leveraging quan tum algorithms. 4. Propose an iterative flow and final fine-tuning step, con tributing to the optimization and continuous improvement of the Quantum Federated Learning process