  Heterogeneous data is endemic due to the use of diverse models and settings
of devices by hospitals in the field of medical imaging. However, there are few
open-source frameworks for federated heterogeneous medical image analysis with
personalization and privacy protection simultaneously without the demand to
modify the existing model structures or to share any private data. In this
paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized
and privacy-preserving federated heterogeneous medical image analysis. To our
best knowledge, personalization and privacy protection were achieved
simultaneously for the first time under the federated scenario by integrating
the PerFedAvg algorithm and designing our novel cyclic secure aggregation with
the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we
applied it to a simulated classification task namely the classification of
healthy people and patients from the RAD-ChestCT Dataset, and one real-world
segmentation task namely the segmentation of lung infections from COVID-19 CT
scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score
on average compared to conventional FL under the heterogeneous scenario.
Meanwhile, we applied the improved deep leakage from gradients to simulate
adversarial attacks and showed the solid privacy-preserving capability of
PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks,
a varied number of users, and sample sizes, we further demonstrated the strong
robustness of PPPML-HMI.
