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Local differential privacy (LDP) is an emerging privacy standard to protectindividual user data. One scenario where LDP can be applied is federatedlearning, where each user sends in his/her user gradients to an aggregator whouses these gradients to perform stochastic gradient descent. In a case wherethe aggregator is untrusted and LDP is not applied to each user gradient, theaggregator can recover sensitive user data from these gradients. In this paper,we present a new interactive web demo showcasing the power of localdifferential privacy by visualizing federated learning with local differentialprivacy. Moreover, the live demo shows how LDP can prevent untrustedaggregators from recovering sensitive training data. A measure called theexp-hamming recovery is also created to show the extent of how much data theaggregator can recover.

Attacks to Federated Learning: Responsive Web User Interface to Recover Training Data from User Gradients