While theapplication ofdifferential privacy(DP) hasbeen well-studied incrossdevice federated learning (FL), there is a lack of work considering DP and its implications for cross-silo FL, a setting characterized by a limited number of clients each containing many data subjects.

In this paper, we find an appealing way to synthesize [JO19] and [CLM19] to give the best of both worlds: an algorithm which runs in polynomial time and can exploit structure in the underlying distribution to achieve sublinear sample complexity.

In this paper, we introduce a novel approach to fine-grained cross-view geo-localization.

In this work, we propose aframework to improvethe certified safety region for these smoothed classifiers without changing the underlying smoothing scheme.

Motivated by recent applications requiring differential privacy over adaptive streams, we investigate optimal instantiations of the matrix mechanism [1] in this setting. Weprovefundamental theoretical results ontheapplicability ofmatrix factorizations to adaptive streams, and provide a parameter-free fixed-point algorithm for computing optimal factorizations.