Can we share quantum intelligence without leaking data?

CS‑QFL connects university labs, hospitals and research centers in a secure network. Each node keeps its data private while contributing to a shared quantum model. Quantum homomorphic encryption lets computations happen on encrypted data. Variational quantum circuits (VQCs) adjust model parameters using quantum noise. The protocol trains models without ever revealing raw datasets. In simulations with five silos, the global model reached 94.2% accuracy. This matches centralized results within a 1.5% margin. Communication dropped by 38% compared to classic federated learning. Compressed quantum state parameters reduce bandwidth needs. Security checks show state fidelity above 0.992. Such fidelity blocks eavesdroppers and parameter‑reconstruction attacks. The approach blends decentralization with quantum‑grade protection. Students can explore how quantum tools reshape collaborative AI. Future work will scale the network to dozens of institutions. Open‑source releases will let anyone test and extend the protocol. Researchers first encode local data into quantum states using reversible gates. These states travel through a trusted quantum channel to an aggregation hub. The hub applies homomorphic operations without decrypting the information. VQCs then perform gradient‑like updates by optimizing quantum circuit parameters. Because the encryption is quantum, even powerful computers cannot infer the data. The simulation used realistic noise models from superconducting qubits. Results show that quantum compression preserves classification performance. Energy consumption stays low thanks to short quantum circuit depth. The protocol also supports differential privacy layers for extra safety. Educators can use CS‑QFL as a case study for quantum cybersecurity courses. Students learn how mathematical theory meets hardware in real‑world apps.

The CS‑QFL protocol leverages two key technologies: quantum homomorphic encryption (QHE) and variational quantum circuits (VQC). QHE allows logical operations to be applied directly on encrypted data using quantum keys, eliminating the need to decrypt information during training. VQCs optimize a parameterized quantum circuit by varying weights through classical optimization algorithms, generating gradients that reflect the distributed quantum state structure. In the network layer, each silo transmits a highly compressed quantum parameter vector, calculated via quantum error correction coding, drastically cutting communication overhead. State fidelity, defined as ⟨ψ|ρ|ψ⟩, stays above 0.992 thanks to quantum‑secure aggregation, which combines quantum signatures with a Bloch‑rotation mixing mechanism. This fidelity ensures aggregation does not introduce significant errors, protecting the model from man‑in‑the‑middle attacks and classical parameter reconstruction. Formal security analyses show that the conditional entropy of the quantum key remains high, providing post‑quantum security guarantees. Experimental results on five independent silos achieve a classification accuracy of 94.2% on distributed quantum datasets, matching centralized baselines within a 1.5% margin. The 38% reduction in communication traffic stems directly from quantum compression and the smaller size of parameter vectors. Future work will extend the protocol to hundreds of nodes, integrate satellite quantum networks, and apply it to biomedical and financial domains where privacy and security are paramount.