Fast and privacy preserving distributed low-rank regression

Hoi To Wai, Anna Scaglione, Jean Lafond, Eric Moulines

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper proposes a fast and privacy preserving distributed algorithm for handling low-rank regression problems with nuclear norm constraint. Traditional projected gradient algorithms have high computation costs due to their projection steps when they are used to solve these problems. Our gossip-based algorithm, called the fast DeFW algorithm, overcomes this issue since it is projection-free. In particular, the algorithm incorporates a carefully designed decentralized power method step to reduce the complexity by distributed computation over network. Meanwhile, privacy is preserved as the agents do not exchange the private data, but only a random projection of them. We show that the fast DeFW algorithm converges for both convex and non-convex losses. As an application example, we consider the low-rank matrix completion problem and provide numerical results to support our findings.

Original languageEnglish (US)
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4451-4455
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - Jun 16 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: Mar 5 2017Mar 9 2017

Other

Other2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period3/5/173/9/17

Keywords

  • distributed optimization
  • Frank-Wolfe algorithm
  • gossip algorithms
  • low-rank regression
  • power method

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Wai, H. T., Scaglione, A., Lafond, J., & Moulines, E. (2017). Fast and privacy preserving distributed low-rank regression. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings (pp. 4451-4455). [7952998] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2017.7952998