The recent expansion of the IoT ecosystem has not only significantly increased connectivity but also introduced new security challenges. To address emerging security challenges, this study proposes a framework that merges the decentralized methodologies of federated learning (FL) and Blockchain. The framework is rigorously tested and validated on the N-BaIoT Dataset employing dense neural networks (DNNs) and logistic regression (LR). This approach decentralizes the training of machine learning (ML) models by distributing the process across individual IoT devices, this enhances the security and privacy of data. The use of Blockchain ensures transparent and secure management of these decentralized models, adding an extra layer of protection against tampering. In addition, this research introduces two novel metrics, namely the Security Efficacy Metric and the Comparative Improvement Factor, which provide a quantitative foundation for evaluating the performance of the proposed framework. The examination of the proposed framework through LR and DNNs demonstrates significant results. The LR model achieved a global accuracy of 99.98%, with an average client data size of 440.95 MB and a model size of 0.00088 MB. Meanwhile, the DNN model exhibited a global accuracy of 99.99%, with an average client data size of 551.95 MB and a model size of 0.09 MB. This research contributes to IoT security by integrating LR and DNNs within the FL setup, complemented by blockchain technology, signifying a substantial advancement in the dynamic IoT ecosystem.

物联网生态系统最近的扩张不仅显着增加了连接性，而且带来了新的安全挑战。为了应对新出现的安全挑战，本研究提出了一个融合联邦学习（FL）和区块链的去中心化方法的框架。该框架使用密集神经网络 (DNN) 和逻辑回归 (LR) 在 N-BaIoT 数据集上经过严格测试和验证。这种方法通过将流程分布在各个物联网设备上来分散机器学习 (ML) 模型的训练，从而增强了数据的安全性和隐私性。区块链的使用确保了这些去中心化模型的透明和安全管理，增加了一层额外的防篡改保护。此外，本研究引入了两个新颖的指标，即安全效能指标和比较改进因子，为评估所提出的框架的性能提供了定量基础。通过 LR 和 DNN 对所提出的框架进行的检查显示出显着的结果。 LR模型的全局准确率达到99.98%，平均客户端数据大小为440.95 MB，模型大小为0.00088 MB。同时，DNN模型的全局准确率达到99.99%，平均客户端数据大小为551.95 MB，模型大小为0.09 MB。这项研究通过在 FL 设置中集成 LR 和 DNN，并辅以区块链技术，为物联网安全做出了贡献，标志着动态物联网生态系统的重大进步。