The role of Omnichain in advancing federated learning for artificial intelligence training in healthcare

Abstract

Health data serves as a crucial foundation for artificial intelligence (AI) training in the healthcare sector. The pivotal procedure for acquiring numerous and effective health data lies in incentivizing participants to contribute their health data while adhering to privacy regulations like the General Data Protection Regulation. Federated learning achieves privacy protection by transmitting only parameters rather than data to the model. When integrated with blockchain smart contracts, this approach facilitates the automation of incentives according to health data quality, thereby mitigating human’s subjective intervention. Consequently, the synergy of these two methodologies offers new promise for the training of AI models in healthcare. However, this advantage encounters performance degradation due to the heterogeneity among diverse blockchains. This article posits the concept of Omnichain as a potential solution to this challenge by analyzing its operational mechanisms and future developmental trajectories and providing potential perspectives for its combination with hybrid federal learning solutions such as differential privacy and secure multi-party computation to promote its application in the sphere of AI in healthcare.

Keywords

Omnichain / Federated learning / Artificial intelligence training / Healthcare / Training performance

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

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