While traffic sign recognition systems play a vital role in road safety and autonomous driving, traditional architectures often suffer severe accuracy degradation under adverse environmental conditions such as low light, fog, and heavy shadows. Although federated deep learning and convolutional neural networks (CNNs) have successfully advanced decentralized edge intelligence, standard RGB image processing remains a critical bottleneck for vehicles encountering environmental noise. To address this, we propose a lightweight, decentralized ResNet-34 architecture designed for embedded applications, enhanced by a robust multi-space pixel-level pre-processing pipeline. By incorporating localized edge contrast enhancement and chromatic variance stabilization (utilizing HSV and Ohta spaces), the proposed system isolates critical luminance and structural features prior to decentralized feature extraction. The framework was trained and evaluated on the German Traffic Sign Recognition Benchmark (GTSRB) and the Belgian Traffic Sign Data Set (BTSD). The results demonstrate that coupling dynamic image pre-processing with federated residual learning yields a highly efficient, accurate, and environmentally resilient system suitable for real-time edge deployment.