Background:
As artificial intelligence and big data analysis develop rapidly, data privacy, especially patient medical data privacy, is getting more and more attention.
Objective:
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 patient data.
Method:
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.
Upper bounds on the leakage of private data and an operational approach to Markovianity
