Background: As artificial intelligence and big data analysis develop rapidly, data
privacy, especially patient medical data privacy, is getting more and more attention.
Objective: The study aims to strengthen the protection of private data while ensuring the model
training process; this article introduces a multi-Blockchain-based decentralized collaborative
machine learning training method for medical image analysis. In this way, researchers from
different medical institutions are able to collaborate to train models without exchanging sensitive
Methods: Partial parameter update method is applied to prevent indirect privacy leakage during
model propagation. With the peer-to-peer communication in the multi-Blockchain system, a
machine learning task can leverage auxiliary information from another similar task in another
Blockchain. In addition, after the collaborative training process, personalized models of different
medical institutions will be trained.
Results: The experimental results show that our method achieves similar performance with the
centralized model-training method by collecting data sets of all participants and prevents private
data leakage at the same time. Transferring auxiliary information from similar task on another
Blockchain has also been proven to effectively accelerate model convergence and improve model
accuracy, especially in the scenario of absence of data. Personalization training process further
improves model performance.
Conclusion: Our approach can effectively help researchers from different organizations to achieve
collaborative training without disclosing their private data.