Federated Hierarchical Tensor Networks: a Collaborative Learning Quantum AI-Driven Framework for Healthcare

arXiv:2405.07735v1 Announce Type: cross
Abstract: Healthcare industries frequently handle sensitive and proprietary data, and due to strict privacy regulations, they are often reluctant to share data directly. In today's context, Federated Learning (FL) stands out as a crucial remedy, facilitating the rapid advancement of distributed machine learning while effectively managing critical concerns regarding data privacy and governance. The fusion of federated learning and quantum computing represents a groundbreaking interdisciplinary approach with immense potential to revolutionize various industries, from healthcare to …

abstract advancement ai-driven arxiv collaborative context cs.ai cs.lg data distributed federated learning framework healthcare hierarchical industries machine machine learning networks privacy proprietary quant-ph quantum quantum ai regulations tensor type while