Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy.

Traditional centralized road monitoring systems are hindered by significant limitations, with assessment speeds of only 3 to 4 km/h, while 39% of major U.S. roads remain in poor condition, resulting in a ${\$}684$ billion funding gap. This study introduces an enhanced federated learning framework, building upon the FedProx algorithm, for privacy-preserving road condition detection in connected vehicles. Our approach enables collaborative model training across distributed vehicle networks using accelerometer, gyroscope, and GPS sensor data, while reducing data exposure by keeping raw sensor data on-device. We developed the Federated Learning Model and trained it on a comprehensive dataset comprising 28,523 samples across three road conditions: bumps, potholes, and normal, which were collected from smartphone sensors. The proposed architecture outperforms existing methods, achieving 98.18% accuracy for pothole detection and 97.53% for bump detection, surpassing FedAvg and FedSGD by up to 5.6%. Experimental validation with small-scale federations (5 to 15 clients) demonstrates the framework’s effectiveness and validates the core algorithmic contributions. The proposed architecture is designed to scale to larger deployments and addresses fundamental challenges of centralized systems. This approach enables real-time road condition monitoring among several vehicles, overcomes the scalability challenges of centralized systems, and offers potential annual cost savings of ${\$}2.1$ billion by leveraging 90% connected vehicle penetration by 2030.