This podcast discusses the application of Quantum Tensor Networks in Federated Learning for healthcare, providing a collaborative and privacy-preserving framework for analyzing medical data and improving diagnostic tools. Key Points Federated Learning (FL) offers a solution for healthcare institutions to collaborate on analyzing sensitive data while maintaining privacy and reducing data transfer costs. The integration of Quantum Tensor Networks (QTNs) in this framework shows promise in successfully training models on heterogeneous medical data across multiple healthcare institutions. The experiments conducted on different medical datasets demonstrate the superior performance of the Quantum Federated Global Model, particularly with models like TTN and MERA, showcasing higher accuracy and improved generalization compared to locally trained models.