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How Much Noise is Enough: On Privacy, Security, and Accuracy Trade-Offs in Differentially Private Federated Learning

Adhishree Kathikar Kyle Chard Nathaniel Hudson
· 0 citations · 4 references

TL;DR

This work applies differential privacy (which adds noise to the model weights before sending across the network) as an added privacy measure to protect sensitive data from being reconstructed in Federated Learning.

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