In the day-to-day operations of healthcare institutions, a multitude of Personally Identifiable Information (PII) data exchanges occur, exposing the data to a spectrum of cybersecurity threats. This study introduces a federated learning framework, trained on the Wisconsin dataset, to mitigate challenges such as data scarcity and imbalance. Techniques like the Synthetic Minority Over-sampling Technique (SMOTE) are incorporated to bolster robustness, while isolation forests are employed to fortify the model against outliers. Catboost serves as the classification tool across all devices. The identification of optimal features for heightened accuracy is pursued through Principal Component Analysis (PCA),accentuating the significance of hyperparameter tuning, as underscored in a comparative analysis. The model exhibits an average accuracy of 99.95% on edge devices and 98% on the central server.