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Mendata: A Framework to Purify Manipulated Training Data
Untrusted data used to train a model might have been manipulated to endow the learned model with hidden properties that the data contributor might later exploit.
Differentially Private ADMM for Convex Distributed Learning: Improved Accuracy via Multi-Step Approximation
Alternating Direction Method of Multipliers (ADMM) is a popular algorithm for distributed learning, where a network of nodes collaboratively solve a regularized empirical risk minimization by iterative local computation associated with distributed data and iterate exchanges.
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
The goal of this paper is to provide differential privacy for ADMM-based distributed machine learning.
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