With the advent of the intelligent information processing era, more and more private sensitive data are being collected and analyzed for intelligent decision making tasks. Such information processing also brings many challenges with existing privacy protection algorithms. On the one hand, the algorithms based on data encryption compromise the integrity of the original data or incur high computational and communication costs to some extent. On the other hand, algorithms based on distributed learning require frequent sharing of parameters between different computing nodes, which poses risks of leaking local model information and reducing global learning efficiency. To mitigate the impact of these issues, a Quantum Fuzzy Federated Learning (QFFL) algorithm is proposed. In the QFFL algorithm, a Quantum Fuzzy Neural Network (QFNN) is designed at the local computing nodes, which enhances data generalization while preserving data integrity. In global model, QFFL makes predictions through the Quantum Federated Inference (QFI). QFI leads to a general framework for quantum federated learning on non-IID data with oneshot communication complexity, achieving privacy protection of local data and accelerating the global learning efficiency of the algorithm. The experiments are conducted on the COVID19 and MNIST datasets, and the results indicate that QFFL demonstrates superior performance compared to the baselines, manifesting in faster training efficiency, higher accuracy, and enhanced security. In addition, based on the fidelity experiments and related analysis under four common quantum noise channels, the results demonstrated that it has good robustness against quantum noises, proving its applicability and practicality. Our code is available at https://github.com/LASTsue/QFFL. © IEEE