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Found 3 papers, 3 papers with code
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
In this work, we initiate the study of federated synthetic tabular data generation.
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
We propose a novel method, CANIFE, that uses canaries - carefully crafted samples by a strong adversary to evaluate the empirical privacy of a training round.
Federated Boosted Decision Trees with Differential Privacy
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data.
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