ESecDT: Communication-Efficient and Secure Decision Tree Training Framework

ESecDT is a framework that allows multiple parties to train decision trees (machine learning models used to make predictions by sorting data into categories) together while keeping their individual data private. It combines two cryptographic techniques called Function Secret Sharing (FSS, a method where a secret is split into parts that only work together) and Replicated Secret Sharing (RSS, another way to distribute secrets across parties) to reduce the amount of data that must be sent between parties during training while maintaining strong privacy protections.