Privacy-preserving blockchain-enabled federated learning for B5G-Driven edge computing

【Author】 Wan, Yichen; Qu, Youyang; Gao, Longxiang; Xiang, Yong

【Source】COMPUTER NETWORKS

【Abstract】The arrival of the fifth-generation technology standard for broadband cellular networks (5G) and beyond 5G networks (B5G) rises the speed and robustness ceiling of communicating networks and thereby empowers the rapid popularization of edge computing. Consequently, B5G-Driven edge computing allows a growing volume of data to be collected from and transmitted among pervasive edge devices for big data analytics. The collected big data becomes the driving force of artificial intelligence (AI) by training high-quality machine learning (ML) models, which is followed by severe individual privacy leakage. Federated learning(FL) is then proposed to achieve privacy-preserving machine learning by avoiding the exchange of raw data. Unfortunately, several major issues remain outstanding. Centralized processing costs significant communication resources between cloud and edge while data falsification problems persist. In addition, the private data may be reconstructed by malicious participants by exploiting the context of model parameters in FL. To solve the identified problems, we propose to integrate blockchain-enabled FL with Wasserstein generative adversarial network (WGAN) enabled differential privacy (DP) to protect the model parameters of edge devices in B5G networks. Blockchain enables decentralized FL to reduce communication costs between cloud and edge while alleviating the data falsification issues, and it also provides an incentive mechanism to alleviate the data island issue in B5G-Driven edge computing. WGAN is used to generate controllable random noise complying with DP requirements, which is then injected to model parameters. WGAN-enabled DP is able to achieve an optimized trade-off between differential privacy protection and improved data utility of model parameters. Time delay analysis is conducted to show the efficiency of the proposed model. Extensive evaluation results from simulations demonstrate superior performances from aspects of convergence efficiency, accuracy, and data utility.

【Keywords】Blockchain; Federated learning; Differential privacy protection; Wasserstein generative adversarial nets

【标题】用于 B5G 驱动的边缘计算的隐私保护区块链联邦学习

【摘要】第五代宽带蜂窝网络（5G）及超越5G网络（B5G）的技术标准的到来，提高了通信网络的速度和鲁棒性上限，从而使边缘计算迅速普及。因此，B5G 驱动的边缘计算允许从无处不在的边缘设备中收集和传输越来越多的数据，以进行大数据分析。收集到的大数据通过训练高质量的机器学习（ML）模型成为人工智能（AI）的驱动力，随之而来的是严重的个人隐私泄露。然后提出了联邦学习（FL），通过避免交换原始数据来实现隐私保护机器学习。不幸的是，几个主要问题仍然悬而未决。集中处理会消耗大量云和边缘之间的通信资源，而数据伪造问题仍然存在。此外，恶意参与者可能会通过利用 FL 中模型参数的上下文来重建私有数据。为了解决已识别的问题，我们建议将启用区块链的 FL 与启用差异隐私 (DP) 的 Wasserstein 生成对抗网络 (WGAN) 集成，以保护 B5G 网络中边缘设备的模型参数。区块链使去中心化 FL 能够降低云与边缘之间的通信成本，同时缓解数据伪造问题，并提供激励机制来缓解 B5G 驱动的边缘计算中的数据孤岛问题。 WGAN用于生成符合DP要求的可控随机噪声，然后将其注入模型参数。启用 WGAN 的 DP 能够在差分隐私保护和模型参数的改进数据效用之间实现优化权衡。进行时间延迟分析以显示所提出模型的效率。模拟的广泛评估结果证明了收敛效率、准确性和数据实用性方面的卓越性能。

【关键词】区块链；联邦学习；差异化隐私保护； Wasserstein 生成对抗网络

【发表时间】2022

【收录时间】2022-07-06

【文献类型】Article

【论文大主题】区块链联邦学习

【论文小主题】联邦学习为主体

【影响因子】5.493

【翻译者】石东瑛