【Author】
Xuan, Shichang; Jin, Ming; Li, Xin; Yao, Zhaoyuan; Yang, Wu; Man, Dapeng
【Source】SECURITY AND COMMUNICATION NETWORKS
【Abstract】The rapid development in network technology has resulted in the proliferation of Internet of Things (IoT). This trend has led to a widespread utilization of decentralized data and distributed computing power. While machine learning can benefit from the massive amount of IoT data, privacy concerns and communication costs have caused data silos. Although the adoption of blockchain and federated learning technologies addresses the security issues related to collusion attacks and privacy leakage in data sharing, the free-rider attacks and model poisoning attacks in the federated learning process require auditing of the training models one by one. However, that increases the communication cost of the entire training process. Hence, to address the problem of increased communication cost due to node security verification in the blockchain-based federated learning process, we propose a communication cost optimization method based on security evaluation. By studying the verification mechanism for useless or malicious nodes, we also introduce a double-layer aggregation model into the federated learning process by combining the competing voting verification methods and aggregation algorithms. The experimental comparisons verify that the proposed model effectively reduces the communication cost of the node security verification in the blockchain-based federated learning process.
【标题】DAM-SE：基于区块链的联邦学习系统互联网逆袭优化解决方案
【摘要】网络技术的飞速发展导致了物联网（IoT）的普及。这种趋势导致了去中心化数据和分布式计算能力的广泛使用。虽然机器学习可以从大量的物联网数据中受益，但隐私问题和通信成本已经导致数据孤岛。虽然采用区块链和联邦学习技术解决了数据共享中的共谋攻击和隐私泄露等安全问题，但联邦学习过程中的搭便车攻击和模型中毒攻击需要对训练模型进行一一审计。但是，这增加了整个培训过程的沟通成本。因此，为了解决基于区块链的联邦学习过程中节点安全验证导致通信成本增加的问题，我们提出了一种基于安全评估的通信成本优化方法。通过研究无用或恶意节点的验证机制，我们还将竞争投票验证方法和聚合算法相结合，将双层聚合模型引入联邦学习过程。实验对比验证了所提模型有效降低了基于区块链的联邦学习过程中节点安全验证的通信成本。
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