Research on Traditional Chinese Medicine Data Element Inheritance and Innovation Strategy under the Federal Learning Framework
Objective To explore how to deal with the challenges in the process of Traditional Chinese Medicine(TCM)data element inheritance and innovation in the period of digital transformation.Methods The potential of federated learning technologies was investigated.The issues of data scarcity,data silos,and privacy breaches were addressed through its distributed cooperation mechanisms to allow the participants to share model updates while maintaining local data storage.Results The application of feder-ated learning is helpful to promote data fusion and technological innovation of TCM on the basis of ensuring data security.At the same time,its advantages and feasibility of accelerating the digital and intelligent transformation of traditional Chinese medicine were analyzed.Conclusion Under the premise of protecting privacy,combined with new quality productivity,federal learning can further stimulate the effective use of TCM data elements,promote the circulation of TCM data elements,realize data-driven sci-entific and technological progress,and build a more open and shared TCM research and application ecology.
TCM data elementsfederal learninginheritance and innovationdata island
NETL
NSTL
万方数据
