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.
  • Bongjun Ko (IBM US)
  • Shiqiang Wang (IBM US)
  • Ting He (PSU)
  • Dave Conway-Jones (IBM UK)
Date Jun-2019
Venue 2019 IEEE International Conference on Smart Computing (SMARTCOMP)