Federated Learning Empowered Breast Cancer Detection in Images: A YOLO and ResNet-50 Fusion Approach

In the world, breast cancer kills more women than any other disease. An accurate forecast model was created, and the highest risk was identified by a data mining-based classification effort that used many approaches. The development of automated breast cancer diagnostic tools has been enabled by advances in data mining and machine learning. It is possible to tell benign breast lesions from malignant ones during a breast cancer diagnosis. More than that, people who have had tumors surgically removed can be given prognoses that tell them when the cancer will return. Consequently, the issues surrounding categorization include these two concerns. There are a lot of data mining techniques used to categorize patients as either "cancerous" or "non-cancerous" in this field. Breast cancer diagnostic concerns are thus encapsulated in the domain of hotly debated classification issues. This work used deep learning to diagnose breast cancer via federated learning while protecting user and hospital data. This study uses YOLO and RestNet-50 DL models. YOLO trains RestNet using client data. The server receives client-trained models for global model integration. Integration gives the client access to the global model for performance evaluation. The suggested model outperforms another state-of-the-art deep learning model in the identical scenario with 98.73% accuracy when all clients receive data equally. The federated research environment has hyper-performance elements like many clients and communication cycles.