@inproceedings{d25ebf4e03644718a2f44b36b1c2a138,
title = "Large scale collaboration with autonomy: Decentralized data ICA",
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.",
keywords = "ICA, decentralized data, multi-site collaboration",
author = "Baker, {Bradley T.} and Silva, {Rogers F.} and Calhoun, {Vince D.} and Sarwate, {Anand D.} and Plis, {Sergey M.}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 ; Conference date: 17-09-2015 Through 20-09-2015",
year = "2015",
month = nov,
day = "10",
doi = "10.1109/MLSP.2015.7324344",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
editor = "Deniz Erdogmus and Serdar Kozat and Jan Larsen and Murat Akcakaya",
booktitle = "2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015",
}