  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.
