Learning Privately over Distributed Features: An ADMM Sharing Approach
This work addresses privacy concerns in distributed learning for scenarios where features are split across parties, offering a practical solution with incremental improvements over existing methods.
The paper tackles the problem of distributed machine learning with vertically partitioned features while preserving privacy, proposing an ADMM sharing framework that minimizes data leakage by sharing only a single value per sample. The method demonstrates efficient convergence and robustness, outperforming gradient-based methods on high-dimensional datasets.
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.