An asynchronous federated learning-assisted data sharing method for medical blockchain

IF 3.4 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Chenquan Gan, Xinghai Xiao, Yiye Zhang, Qingyi Zhu, Jichao Bi, Deepak Kumar Jain, Akanksha Saini
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引用次数: 0

Abstract

Currently, medical blockchain data sharing methods that rely on federated learning face challenges, including node disconnection, vulnerability to poisoning attacks, and insufficient consideration of conflicts of interest among participants. To address these issues, we propose a novel method for data sharing in medical blockchain systems based on asynchronous federated learning. First, we develop an aggregation algorithm designed specifically for asynchronous federated learning to tackle the problem of node disconnection. Next, we introduce a Proof of Reputation (PoR) consensus algorithm and establish a consensus committee to mitigate the risk of poisoning attacks. Furthermore, we integrate a tripartite evolutionary game model to examine conflicts of interest among publishing nodes, committee nodes, and participating nodes. This framework enables all parties involved to make strategic decisions that promote sustainable data-sharing practices. Finally, we conduct a security analysis to validate the theoretical effectiveness of the proposed method. Experimental evaluations using real medical datasets demonstrate that our method outperforms existing approaches.

Abstract Image

Abstract Image

一种异步联邦学习辅助医疗bbb数据共享方法
目前,依赖于联邦学习的医疗区块链数据共享方法面临着诸多挑战,包括节点断开、易受中毒攻击以及对参与者之间利益冲突考虑不足。为了解决这些问题,我们提出了一种基于异步联邦学习的医疗区块链系统数据共享的新方法。首先,我们开发了一个专门为异步联邦学习设计的聚合算法来解决节点断开的问题。接下来,我们引入声誉证明(PoR)共识算法,并建立共识委员会来降低中毒攻击的风险。此外,我们整合了一个三方演化博弈模型来检验发布节点、委员会节点和参与节点之间的利益冲突。该框架使有关各方能够作出战略决策,促进可持续的数据共享做法。最后,我们进行了一个安全性分析来验证所提出方法的理论有效性。使用真实医疗数据集的实验评估表明,我们的方法优于现有的方法。
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来源期刊
Applied Intelligence
Applied Intelligence 工程技术-计算机:人工智能
CiteScore
6.60
自引率
20.80%
发文量
1361
审稿时长
5.9 months
期刊介绍: With a focus on research in artificial intelligence and neural networks, this journal addresses issues involving solutions of real-life manufacturing, defense, management, government and industrial problems which are too complex to be solved through conventional approaches and require the simulation of intelligent thought processes, heuristics, applications of knowledge, and distributed and parallel processing. The integration of these multiple approaches in solving complex problems is of particular importance. The journal presents new and original research and technological developments, addressing real and complex issues applicable to difficult problems. It provides a medium for exchanging scientific research and technological achievements accomplished by the international community.
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