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Learning Privately over Distributed Features: An ADMM Sharing Approach

Hu, Yaochen, Liu, Peng, Ge, Keshi, Kong, Linglong, Jiang, Bei, Niu, Di (2020) Learning Privately over Distributed Features: An ADMM Sharing Approach. In: 34th Conference on Neural Information Processing Systems (NeurIPS 2020). . (KAR id:76239)

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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 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 features.

Item Type: Conference or workshop item (Proceeding)
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Peng Liu
Date Deposited: 06 Sep 2019 11:24 UTC
Last Modified: 25 Apr 2022 11:50 UTC
Resource URI: (The current URI for this page, for reference purposes)
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