Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data

arXiv:2402.00205v2 Announce Type: replace-cross
Abstract: Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without …

accuracy analysis arxiv cs.cr cs.lg data data analysis datasets great healthcare hospital institutions large machine machine learning medical medical data ml models privacy settings sharing