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What is Enhancing Federated Learning Privacy about?

This paper discusses the integration of Differential Privacy (DP) and Homomorphic Encryption (HE) in Federated Learning (FL) to enhance data privacy while addressing potential adversarial threats from other clients. The authors introduce a novel stochastic quantization operator that maintains DP guarantees while utilizing HE, demonstrating its effectiveness through experiments on the FEMNIST dataset. The findings suggest that this combined approach can improve the security of FL frameworks without significa

Author

koteshb235

Language

EN