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  1. ; ; ; (, 19th Annual Symposium on Information Assurance (ASIA’ 24) , June 4-5, 2024, Albany, NY)
    Goal, S (Ed.)
    Machine Learning models are widely utilized in a variety of applications, including Intelligent Transportation Systems (ITS). As these systems are operating in highly dynamic environments, they are exposed to numerous security threats that cause Data Quality (DQ) variations. Among such threats are network attacks that may cause data losses. We evaluate the influence of these factors on the image DQ and consequently on the image ML model performance. We propose and investigate Federated Learning (FL) as the way to enhance the overall level of privacy and security in ITS, as well as to improve ML model robustness to possible DQ variations in real-world applications. Our empirical study conducted with traffic sign images and YOLO, VGG16 and ResNet models proved the greater robustness of FL-based architecture over a centralized one. 
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