Decentralised, Collaborative, and Privacy-preserving Machine Learning
  for Multi-Hospital Data
Congyu Fang Adam Dziedzic Lin Zhang Laura Oliva Amol Verma Fahad Razak Nicolas Papernot Bo Wang
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 the need for direct sharing of those datasets or compromising the
privacy of the datasets through collaboration. In this paper, we address this
challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML
for Multi-Hospital Data (DeCaPH). It offers the following key benefits: (1) it
allows different parties to collaboratively train an ML model without
transferring their private datasets; (2) it safeguards patient privacy by
limiting the potential privacy leakage arising from any contents shared across
the parties during the training process; and (3) it facilitates the ML model
training without relying on a centralized server. We demonstrate the
generalizability and power of DeCaPH on three distinct tasks using real-world
distributed medical datasets: patient mortality prediction using electronic
health records, cell-type classification using single-cell human genomes, and
pathology identification using chest radiology images. We demonstrate that the
ML models trained with DeCaPH framework have an improved utility-privacy
trade-off, showing it enables the models to have good performance while
preserving the privacy of the training data points. In addition, the ML models
trained with DeCaPH framework in general outperform those trained solely with
the private datasets from individual parties, showing that DeCaPH enhances the
model generalizability.