TDLearning:基于区块链智能合约的可信分布式协作学习

IF 2.8 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Future Internet Pub Date : 2023-12-25 DOI:10.3390/fi16010006
Jing Liu, Xuesong Hai, Keqin Li
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引用次数: 0

摘要

海量数据驱动着深度学习模型的性能,但在实际应用中,数据资源往往高度分散,且受到数据隐私和安全问题的限制,多个数据源很难直接共享本地数据。数据资源难以有效聚合,导致模型训练缺乏支持。因此,如何在数据源之间进行协作,以聚合数据资源的价值是一个重要的研究问题。然而,现有的分布式协作学习架构在缺乏互信的节点之间开展协作仍面临严峻挑战,安全和信任问题严重影响了数据源参与协作的信心和意愿。区块链技术提供了可信的分布式存储和计算,将其与数据源之间的协作相结合,构建可信的分布式协作学习架构是一个极具应用价值的研究方向。我们提出了一种基于区块链智能合约的可信分布式协作学习机制。首先,该机制使用区块链智能合约来定义和封装分布式协作节点之间的协作行为、关系和规范。其次,我们提出了一种基于特征融合的模型融合方法,用分布式模型协同训练替代本地数据资源的直接共享,组织分布式数据资源进行分布式协作,提高模型性能。最后,为了验证所提机制的可信性和可用性,一方面,我们利用彩色 Petri 网实现了智能合约的形式化建模和验证,并通过验证与该机制相关的智能合约的形式化模型,证明该机制满足预期的可信性属性。另一方面,基于特征融合的模型融合方法在不同的数据集和协作场景中进行了评估,同时实现了一个典型的协作学习案例,对该机制进行了全面的分析和验证。实验结果表明,所提出的机制可以为缺乏互信的分布式协作节点提供可信、公平的协作基础设施,并组织分散的数据资源进行协作模型训练,从而开发出有效的全局模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TDLearning: Trusted Distributed Collaborative Learning Based on Blockchain Smart Contracts
Massive amounts of data drive the performance of deep learning models, but in practice, data resources are often highly dispersed and bound by data privacy and security concerns, making it difficult for multiple data sources to share their local data directly. Data resources are difficult to aggregate effectively, resulting in a lack of support for model training. How to collaborate between data sources in order to aggregate the value of data resources is therefore an important research question. However, existing distributed-collaborative-learning architectures still face serious challenges in collaborating between nodes that lack mutual trust, with security and trust issues seriously affecting the confidence and willingness of data sources to participate in collaboration. Blockchain technology provides trusted distributed storage and computing, and combining it with collaboration between data sources to build trusted distributed-collaborative-learning architectures is an extremely valuable research direction for application. We propose a trusted distributed-collaborative-learning mechanism based on blockchain smart contracts. Firstly, the mechanism uses blockchain smart contracts to define and encapsulate collaborative behaviours, relationships and norms between distributed collaborative nodes. Secondly, we propose a model-fusion method based on feature fusion, which replaces the direct sharing of local data resources with distributed-model collaborative training and organises distributed data resources for distributed collaboration to improve model performance. Finally, in order to verify the trustworthiness and usability of the proposed mechanism, on the one hand, we implement formal modelling and verification of the smart contract by using Coloured Petri Net and prove that the mechanism satisfies the expected trustworthiness properties by verifying the formal model of the smart contract associated with the mechanism. On the other hand, the model-fusion method based on feature fusion is evaluated in different datasets and collaboration scenarios, while a typical collaborative-learning case is implemented for a comprehensive analysis and validation of the mechanism. The experimental results show that the proposed mechanism can provide a trusted and fair collaboration infrastructure for distributed-collaboration nodes that lack mutual trust and organise decentralised data resources for collaborative model training to develop effective global models.
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来源期刊
Future Internet
Future Internet Computer Science-Computer Networks and Communications
CiteScore
7.10
自引率
5.90%
发文量
303
审稿时长
11 weeks
期刊介绍: Future Internet is a scholarly open access journal which provides an advanced forum for science and research concerned with evolution of Internet technologies and related smart systems for “Net-Living” development. The general reference subject is therefore the evolution towards the future internet ecosystem, which is feeding a continuous, intensive, artificial transformation of the lived environment, for a widespread and significant improvement of well-being in all spheres of human life (private, public, professional). Included topics are: • advanced communications network infrastructures • evolution of internet basic services • internet of things • netted peripheral sensors • industrial internet • centralized and distributed data centers • embedded computing • cloud computing • software defined network functions and network virtualization • cloud-let and fog-computing • big data, open data and analytical tools • cyber-physical systems • network and distributed operating systems • web services • semantic structures and related software tools • artificial and augmented intelligence • augmented reality • system interoperability and flexible service composition • smart mission-critical system architectures • smart terminals and applications • pro-sumer tools for application design and development • cyber security compliance • privacy compliance • reliability compliance • dependability compliance • accountability compliance • trust compliance • technical quality of basic services.
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