Fair compute loads enabled by blockchain: sharing models by alternating client and server roles

【Author】 Kuo, Tsung-Ting; Gabriel, Rodney A.; Ohno-Machado, Lucila

【Source】JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

【影响因子】7.942

【Abstract】Objective: Decentralized privacy-preserving predictive modeling enables multiple institutions to learn a more generalizable model on healthcare or genomic data by sharing the partially trained models instead of patient-level data, while avoiding risks such as single point of control. State-of-the-art blockchain-based methods remove the server role but can be less accurate than models that rely on a server. Therefore, we aim at developing a general model sharing framework to preserve predictive correctness, mitigate the risks of a centralized architecture, and compute the models in a fair way. Materials and Methods: We propose a framework that includes both server and client roles to preserve correctness. We adopt a blockchain network to obtain the benefits of decentralization, by alternating the roles for each site to ensure computational fairness. Also, we developed GloreChain (Grid Binary LOgistic REgression on Permissioned BlockChain) as a concrete example, and compared it to a centralized algorithm on 3 healthcare or genomic datasets to evaluate predictive correctness, number of learning iterations and execution time. Results: GloreChain performs exactly the same as the centralized method in terms of correctness and number of iterations. It inherits the advantages of blockchain, at the cost of increased time to reach a consensus model. Discussion: Our framework is general or flexible and can also address intrinsic challenges of blockchain networks. Further investigations will focus on higher-dimensional datasets, additional use cases, privacy-preserving quality concerns, and ethical, legal, and social implications. Conclusions: Our framework provides a promising potential for institutions to learn a predictive model based on healthcare or genomic data in a privacy-preserving and decentralized way.

【Keywords】blockchain distributed ledger technology; privacy-preserving predictive modeling; batch machine learning; clinical information systems; decision support systems

【发表时间】2019 MAY

【收录时间】2022-01-02

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