@inproceedings{66b9732bac814eca97239435f596728e,
title = "On data summarization for machine learning in multi-organization federations",
abstract = "Machine learning is a promising technology for many modern applications. To train an effective machine learning model, a large amount of data is required. However, data may be created in different organizations and sharing data across organizational boundaries is difficult due to privacy concerns and communication bandwidth limitations. Data summarization is a technique for reducing the amount of data that needs to be shared, while preserving characteristics in the data that are useful for training machine learning models. In this paper, we present an overview of data summarization techniques, which can be useful for machine learning across organizational boundaries. We also discuss some possible applications related to these data summarization techniques and challenges for future research.",
author = "Ko, {Bong Jun} and Shiqiang Wang and Ting He and Dave Conway-Jones",
year = "2019",
month = jun,
doi = "10.1109/SMARTCOMP.2019.00030",
language = "English (US)",
series = "Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "63--68",
booktitle = "Proceedings - 2019 IEEE International Conference on Smart Computing, SMARTCOMP 2019",
address = "United States",
note = "5th IEEE International Conference on Smart Computing, SMARTCOMP 2019 ; Conference date: 12-06-2019 Through 14-06-2019",
}