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Differential Privacy in Federated Learning by tasfiatasnim1084 is a document available to read on EtoBox.

What is Differential Privacy in Federated Learning about?

This paper presents a differential privacy-preserving mechanism for multi-party collaboration in federated learning, addressing privacy concerns in data center networks. It introduces a stochastic forest algorithm and a differential privacy XGBoost (DPXGB) algorithm, both designed to protect user data while maintaining high accuracy. Empirical results demonstrate that the proposed methods outperform existing state-of-the-art approaches in terms of privacy protection and model performance.

Author

tasfiatasnim1084

Language

EN