Enhancing Cancer Detection Capabilities in Medical Consumer Electronics through Split Federated Learning and Deep Learning Optimization

The increasing demand for accurate and accessible cancer detection has driven the development of AI-driven diagnostic systems on consumer-grade medical electronics. However, deploying large-scale deep learning models on these devices presents challenges due to limited computational resources, network constraints, and data privacy concerns. In this study, we propose an innovative Split Federated Learning (SFL) framework that synergistically combines the strengths of federated learning and split learning. The SFL framework introduces a novel hybrid architecture where deep learning models are partitioned between client-side and server-side components, enabling efficient local feature extraction while preserving data privacy through secure, encrypted intermediate feature transmission. Experiments conducted on the BreakHis dataset demonstrate the framework’s effectiveness, with the proposed method achieving a 93.1% accuracy while significantly reducing communication overhead. We further analyze the impact of dimensionality reduction techniques and model partitioning configurations to balance accuracy with efficiency. Results show that the SFL framework provides a practical, scalable solution for distributed cancer detection in real-world medical applications, offering a promising approach for privacy-preserving and bandwidth-efficient AI deployment on medical consumer electronics.