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Found 6 papers, 2 papers with code
SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
In this paper, we analyze the sample and communication complexity of the federated linear stochastic approximation (FedLSA) algorithm.
The Relative Gaussian Mechanism and its Application to Private Gradient Descent
Leveraging this assumption, we introduce the Relative Gaussian Mechanism (RGM), in which the variance of the noise depends on the norm of the output.
Differential Privacy has Bounded Impact on Fairness in Classification
We theoretically study the impact of differential privacy on fairness in classification.
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.
High-Dimensional Private Empirical Risk Minimization by Greedy Coordinate Descent
In this paper, we study differentially private empirical risk minimization (DP-ERM).
Differentially Private Coordinate Descent for Composite Empirical Risk Minimization
In this paper, we propose Differentially Private proximal Coordinate Descent (DP-CD), a new method to solve composite DP-ERM problems.
