Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.
翻译:联邦学习(FL)是一个充满希望的分布式学习解决方案,它只能交换模型参数而不透露原始数据。然而,FL的中央架构容易陷入单一的失败点。此外,FL不审查当地模型的合法性,因此即使一小部分恶意装置也会扰乱全球培训。为了解决FL的这些稳健性问题,我们在本文中提议了一个基于链条的分散式FL框架,称为VBFL, 在一个块链结构中利用两个机制。首先,我们引入了一个新的分散化验证机制,让个人验证员审查当地模型更新的合法性。第二,我们设计了一个专门的证明获取共识机制,在该机制中,利害关系更经常地奖励到诚实的装置,通过增加它们指定附在块圈子上的区块的机会来保护合法的当地模型更新。这些解决方案共同促进在合法装置中更加协调,使富有活力的FL。我们模拟MIST的分类结果证实,用15%的恶意装置,VBFBL达到87%的精确度,比Vanilla FL高7.4x。