Federated Learning-Based Privacy Preservation with Blockchain Assistance in IoT 5G Heterogeneous Networks

【Author】 Arumugam, Sampathkumar; Shandilya, Shishir Kumar; Bacanin, Nebojsa

【Source】JOURNAL OF WEB ENGINEERING

【Abstract】In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring users' privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of users' local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.

【Keywords】Blockchain; 5G network; federated machine; privacy preservation; registration

【标题】物联网 5G 异构网络中基于联邦学习的隐私保护与区块链辅助

【摘要】在隐私受到更大关注的领域，联邦学习，一种用于保护隐私的分布式机器学习策略，被广泛应用于多个隐私关注应用程序中。与此同时，神经架构开始熟悉用于自动调整深度神经网络 (DNN) 架构的深度学习方法。虽然使用神经架构和联邦学习进行搜索遇到了一些挑战，但联邦学习中优化的神经架构研究是广泛需要的。 DNN 在训练此类用户隐私和确保从服务器获得的聚合结果的完整性时面临许多问题。为了解决上述问题，大量的联邦学习技术致力于保护隐私，并应用于不同的情况。尽管如此，它仍然是一个开放的挑战，它使用户能够验证云服务器是否正常运行，同时确保用户在培训时的隐私。联邦学习方法是一种提高准确性和精度的新方法，因为以前的方法未能选择解决方案。在这里，提出了基于区块链的联邦学习的椭圆曲线密码学（ECC-BFL），以在执行联邦学习时确保用户局部梯度的机密性。考虑了分类精度、运行时间、通信开销、计算开销和事务速度等参数。将这些参数获得的值与三种标准方法进行比较，即双配对方法 (BM) 同态密码系统 (HC) 和基于属性签名方案的多权限 (MA-ABS) 与提议的基于区块链的联邦学习的椭圆曲线密码学(ECC-BFL)。结果，所提出的 ECC-BFL 实现了 95% 的分类准确率、65 秒的运行时间、76% 的通信开销、63% 的计算开销和 92% 的事务速度。

【关键词】区块链； 5G网络；联合机；隐私保护；登记

【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】0.575

【翻译者】石东瑛