Skip to main content

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: NeurIPS'20 Workshop on 'Scalability, Privacy, and Security in Federated Learning (NeurIPS-SpicyFL'20)', December 5-12, 2020, Vancouver, Canada. (In press) (KAR id:76239)

PDF Author's Accepted Manuscript
Language: English

Restricted to Repository staff only
Contact us about this Publication
[thumbnail of SpicyFL_2020_paper_69.pdf]
PDF Publisher pdf
Language: English
Download (289kB) Preview
[thumbnail of NIPS_Workshop_submission.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL
http://icfl.cc/SpicyFL/2020

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 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: 16 Feb 2021 14:07 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/76239 (The current URI for this page, for reference purposes)
Liu, Peng: https://orcid.org/0000-0002-0492-0029
  • Depositors only (login required):

Downloads

Downloads per month over past year