A novel framework for approximating ROC and PR curves using quantiles of the score distribution, which can be computed efficiently under secure aggregation and distributed differential privacy, and provides theoretical guarantees on the approximation quality by bounding the Area Error between the true and estimated cur...
FedPS is introduced, a framework for federated data preprocessing based on aggregated statistics that design federated algorithms for feature scaling, encoding, discretization, and missing-value imputation, and extend preprocessing-related models such as Bayesian Linear Regression to both horizontal and vertical FL set...
This work proposes PRoVeFL-a novel, modular FL framework that is Privacy-preserving, Byzantine-Robust, and ensures Verifiable aggregation, and improves runtime over the prior works, Prio and ELSA, based on distributed trust with comparable security guarantees, up to 100x and 10x, respectively.
Harsh Kasyap, Anil Kumar Pradhan, U. Atmaca et al.· 0 citations
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