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What is Privacy-Enhanced Federated Learning Method about?

This paper presents a novel privacy-enhanced federated learning framework called Optimal LDP-FL, which utilizes client self-sampling and data perturbation mechanisms to achieve local differential privacy. The proposed framework addresses the challenges of balancing privacy, model performance, and algorithm efficiency, demonstrating significant reductions in computational and communication costs while maintaining privacy and model utility. Comprehensive experiments validate the effectiveness of the proposed

Author

Mohit

Language

EN