Table 3 Steps for data preprocessing.

From: Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity

Step

Description

Data cleaning

Dropped unnecessary columns (Unnamed: 32, id) and handled missing values

Label encoding

Converted the Diagnosis column to binary values (Malignant: 1, Benign: 0)

Normalization

Applied Z-score normalization to scale features to a standard range

Train-test split

Split the dataset into 80% training and 20% testing subsets

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