Communication-Efficient Hybrid Federated Learning for E-health with Horizontal and Vertical Data Partitioning

arXiv:2404.10110v1 Announce Type: new
Abstract: E-health allows smart devices and medical institutions to collaboratively collect patients' data, which is trained by Artificial Intelligence (AI) technologies to help doctors make diagnosis. By allowing multiple devices to train models collaboratively, federated learning is a promising solution to address the communication and privacy issues in e-health. However, applying federated learning in e-health faces many challenges. First, medical data is both horizontally and vertically partitioned. Since single Horizontal Federated Learning (HFL) or Vertical Federated …

abstract artificial artificial intelligence arxiv communication cs.dc cs.lg data data partitioning devices diagnosis doctors federated learning health hybrid intelligence medical multiple partitioning patients smart solution technologies train type