Abstract
The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose breast cancer at an early stage. To improve generalizability, this study examines the implementation of Federated Learning (FedL) to detect breast cancer. Its performance is compared to a centralized training technique that diagnoses breast cancer. Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. This study examines explicitly how a YOLOv6 model trained with FedL performs across several clients. A new homomorphic encryption and decryption algorithm is also proposed to retain data privacy. A novel pruned YOLOv6 model with FedL is introduced in this study to differentiate benign and malignant tissues. The model is trained on the breast cancer pathological dataset BreakHis and BUSI. The proposed model achieved a validation accuracy of 98% on BreakHis dataset and 97% on BUSI dataset. The results are compared with the VGG-19, ResNet-50, and InceptionV3 algorithms, showing that the proposed model achieved better results. The tests reveal that federated learning is feasible, as FedAvrg trains models of outstanding quality with only a few communication rounds, as shown by the results on a range of model topologies such as ResNet50, VGG-19, InceptionV3, and the proposed Ensembled FedL YOLOv6.