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Security and privacy are greatly enhanced by intrusion detection systems. Now, Machine Learning (ML) and Deep Learning (DL) with Intrusion Detection Systems (IDS) have seen great success due to their high levels of classification accuracy. Nevertheless, because data must be stored and communicated to a centralized server in these methods, the confidentiality features of the system may be threatened. This article proposes a blockchain-based Federated Learning (FL) approach to intrusion detection that maintains data privacy by training and inferring detection models locally. This approach improves the diversity of training data as models are trained on data from different sources. We employed the Scaled Conjugate Gradient Algorithm, Bayesian Regularization Algorithm, and Levenberg-Marquardt Algorithm for training our model. The training weights were then applied to the federated learning model. To …