Learning Privately over Distributed Features: An ADMM Sharing Approach

TL;DR

This paper introduces a privacy-preserving distributed learning framework using ADMM that enables multiple parties to collaboratively train models on distributed features with minimal data sharing, ensuring convergence and privacy guarantees.

Contribution

It proposes a novel ADMM sharing approach for distributed feature learning, including a differentially private version, with theoretical convergence and privacy bounds.

Findings

Efficient convergence of the proposed ADMM algorithms.

Enhanced robustness over gradient-based methods in high-dimensional settings.

Effective privacy guarantees with noise perturbation.

Abstract

Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned among multiple parties, and sharing of raw data or model parameters among parties is prohibited due to privacy concerns. We propose an ADMM sharing framework to approach risk minimization over distributed features, where each party only needs to share a single value for each sample in the training process, thus minimizing the data leakage risk. We establish convergence and iteration complexity results for the proposed parallel ADMM algorithm under non-convex loss. We further introduce a novel differentially private ADMM sharing algorithm and bound the privacy guarantee with carefully designed noise perturbation. The experiments based on a prototype system shows that the proposed ADMM algorithms converge efficiently in a robust fashion, demonstrating advantage over gradient based methods especially for data set with high dimensional feature spaces.