With the increasing data scale in the Industrial Internet of Things, edge computing coordinated with machine learning is regarded as an effective way to raise the novel latency-sensitive services. To ensure the data privacy for frequent service access, federated learning (FL), as a privacy-preserving distributed framework, is integrated into edge computing, enabling user data invisible to the training process. However, sophisticated network attacks threaten deep learning (DL) models by data poison and malicious reasoning, making the DL-based system untrustworthy. To this end, a synergic data filtering method, named Safe, is proposed to deal with the poisoning attacks. Specifically, considering that the distributed support vector machine is at risk of being attacked due to its distribution and openness to communication, edge-cloud empowered FL framework is designed. Then, the alternating direction method of multipliers is deployed to detect attacked devices whose training processes will be interrupted. Moreover, due to the untrustworthiness of label data, the poisoned data in the attacked devices are figured out and filtered by clustering the trusted data with K -means clustering algorithm. Eventually, extensive experiment results proved that the Safe outperforms correlation methods in detection accuracy and trustworthiness.

中文翻译：

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安全：云边缘计算联邦学习的协同数据过滤

随着工业物联网数据规模的不断扩大，边缘计算与机器学习相结合被认为是提升新型时延敏感型服务的有效途径。为了确保频繁访问服务的数据隐私，联邦学习（FL）作为一种保护隐私的分布式框架被集成到边缘计算中，使用户数据对训练过程不可见。然而，复杂的网络攻击通过数据中毒和恶意推理威胁深度学习 (DL) 模型，使得基于 DL 的系统不可信。为此，提出了一种名为Safe的协同数据过滤方法来应对中毒攻击。具体地，考虑到分布式支持向量机由于其分布和对通信的开放性而存在被攻击的风险，设计了边缘云赋能的 FL 框架。然后，部署乘法器的交替方向方法来检测训练过程将被中断的攻击设备。此外，由于标签数据的不可信性，被攻击设备中的中毒数据通过将可信数据与K-均值聚类算法。最终，大量的实验结果证明，Safe 在检测准确性和可信度方面优于相关方法。