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Federated Learning (FL) enables collaborative model training on smart edge devices while preserving data privacy, but it suffers from decreased performance when faced with non-Independent and Identically Distributed (non-IID) data. This paper addresses the problem of the evaluation of aggregation strategies in non-IID FL environments, and it proposes an approach to generation of the skewed datasets with different types of non-IIDness from one dataset: with Feature Distribution Skew; with Label Distribution Skew; with Same Label, Different Features skew; and with Same Features, Different Label skew. The authors also introduce a Modified Federated via Local Batch Normalization (MFedBN), which improves model convergence and robustness across various non-IID data skews by implementing a server-side gradient-style update with several Learning Rate values tested within the aggregated function. Experimental evaluation of the MFedBN strategy was conducted on two heterogeneous datasets, namely, the Commercial Vehicles Sensor dataset designed for monitoring vehicle behavior and the NF-UNSW-NB15 dataset for cybersecurity threat detection. In the majority of cases, the MFedBN algorithm outperformed the baseline FedBN, with test accuracies of up to 85% on the Commercial Vehicles Sensor dataset and 99.98% on the NF-UNSW-NB15 dataset. The model trained with MFedBN showed convergence stability and improved generalization in highly heterogeneous federated environments. The proposed algorithm and data generation methods establish a viable platform for privacy-preserving applications in IoT-based monitoring and network intrusion detection, advancing the validity of Federated Learning in real-world, non-IID conditions. Full article