Large scale collaboration with autonomy: Decentralized data ICA

Bradley T. Baker, Rogers F. Silva, Vince D. Calhoun, Anand D. Sarwate, Sergey M. Plis

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

Abstract

Data sharing for collaborative research systems may not be able to use contemporary architectures that collect and store data in centralized data centers. Research groups often wish to control their data locally but are willing to share access to it for collaborations. This may stem from research culture as well as privacy concerns. To leverage the potential of these aggregated larger data sets, we would like tools that perform joint analyses without transmitting the data. Ideally, these analyses would have similar performance and ease of use as current team-based research structures. In this paper we design, implement, and evaluate a decentralized data independent component analysis (ICA) that meets these criteria. We validate our method on temporal ICA for functional magnetic resonance imaging (fMRI) data; this method shares only intermediate statistics and may be amenable to further privacy protections via differential privacy.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Externally publishedYes
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Other

Other25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • ICA
  • decentralized data
  • multi-site collaboration

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Baker, B. T., Silva, R. F., Calhoun, V. D., Sarwate, A. D., & Plis, S. M. (2015). Large scale collaboration with autonomy: Decentralized data ICA. In D. Erdogmus, S. Kozat, J. Larsen, & M. Akcakaya (Eds.), 2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015 [7324344] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2015-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2015.7324344