PrivFED -- A Framework for Privacy-Preserving Federated Learning in Enhanced Breast Cancer Diagnosis

arXiv:2405.08084v1 Announce Type: new
Abstract: 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 …

arxiv cancer challenges cs.cr cybersecurity cybersecurity threats data dataset diagnosis exchanges exposing federated federated learning framework healthcare information institutions operations personally identifiable information pii privacy spectrum study threats wisconsin