Machine learning, particularly deep learning, has revolutionized a number of fields, including medical diagnostics. In this study, federated learning is employed to address privacy concerns and data access limitations inherent in medical imaging. A simulated FL environment was used to investigate the performance of five pre-trained neural network models: DENSENET121, RESNET18, VGG-NET11, GOOGLENET and INCEPTION-V3. It emphasizes the optimization of training duration as well as the application of lossy image compression techniques such as JPEG in order to improve communication efficiency. We conducted a comparative analysis of the models' performance before and after image compression by evaluating the Area Under the Receiver Operating Characteristic Curve and the training time. According to the results, image compression can maintain or improve model performance while affecting training time, underscoring the trade-offs between model accuracy and computational efficiency.