Blockchain-Empowered Federated Learning Through Model and Feature Calibration
Blockchain-Empowered Federated Learning Through Model and Feature Calibration
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DOI：
10.1109/jiot.2023.3311967
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发表时间：
2024-02
影响因子：
10.6
通讯作者：
Qianlong Wang;Weixian Liao;Y. Guo;Michael McGuire;Wei Yu
中科院分区：
文献类型：
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作者：
Qianlong Wang;Weixian Liao;Y. Guo;Michael McGuire;Wei Yu
随着计算功能强大的边缘设备的激增，边缘计算已被广泛用于各种计算任务。其中，边缘人工智能（AI）已成为一种新趋势，允许本地设备协同工作并构建深度学习模型。联邦学习是分布式机器学习的代表框架之一。然而，现有的联邦学习范式存在几个主要问题。现有的分布式框架依赖于中央服务器来协调计算过程，其中这样的中央节点可能会引起安全问题。联合学习还依赖于若干假设/要求，例如，独立同分布（independent and identical distributed，i.i.d.）数据和模型的同质性。由于越来越多的边缘设备能够使用本地数据训练轻量级模型，因此这些模型通常是异构的。为了应对这些挑战，在本文中，我们开发了一个基于区块链的联邦学习框架，该框架能够以完全分散的方式进行学习，同时考虑到模型异构性和数据异构性。特别地，具有异构校准过程的联合学习框架，即，模型和特征校准（FL-MFC）的开发，使异构模型之间的协作。我们进一步设计了一个使用区块链的两级挖掘过程，以实现安全的去中心化学习过程。实验结果表明，我们提出的系统实现了有效的学习性能下完全异构的环境。
With the proliferation of computationally powerful edge devices, edge computing has been widely adopted for wide-ranging computational tasks. Among these, edge artificial intelligence (AI) has become a new trend, allowing local devices to work cooperatively and build deep learning models. Federated learning is one of the representative frameworks in distributed machine learning paradigms. However, there are several major concerns with existing federated learning paradigms. Existing distributed frameworks rely on a central server to coordinate the computing process, where such a central node may raise security concerns. Federated learning also relies on several assumptions/requirements, e.g., the independent and identically distributed (i.i.d.) data and model homogeneity. Since more and more edge devices are able to train lightweight models with local data, such models are normally heterogeneous. To tackle these challenges, in this article, we develop a blockchain-empowered federated learning framework that enables learning in a fully decentralized manner while taking the model heterogeneity and data heterogeneity into account. In particular, a federated learning framework with a heterogeneous calibration process, i.e., Model and Feature Calibration (FL-MFC), is developed to enable collaboration among heterogeneous models. We further design a two-level mining process using blockchain to enable the secure decentralized learning process. Experimental results show that our proposed system achieves effective learning performance under a fully heterogeneous environment.