Browsing by Author "Seth, Avi"

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    Data Sharing and Retrieval of Manufacturing Processes
    Seth, Avi (Virginia Tech, 2023-03-28)
    With Industrial Internet, businesses can pool their resources to acquire large amounts of data that can then be used in machine learning tasks. Despite the potential to speed up training and deployment and improve decision-making through data-sharing, rising privacy concerns are slowing the spread of such technologies. As businesses are naturally protective of their data, this poses a barrier to interoperability. While previous research has focused on privacy-preserving methods, existing works typically consider data that is averaged or randomly sampled by all contributors rather than selecting data that are best suited for a specific downstream learning task. In response to the dearth of efficient data-sharing methods for diverse machine learning tasks in the Industrial Internet, this work presents an end-to end working demonstration of a search engine prototype built on PriED, a task-driven data-sharing approach that enhances the performance of supervised learning by judiciously fusing shared and local participant data.
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    A Task-Driven Privacy-Preserving Data-Sharing Framework for the Industrial Internet
    Shojaee, Parshin; Zeng, Yingyan; Wahed, Muntasir; Seth, Avi; Jin, Ran; Lourentzou, Ismini (2023-01)
    Industrial Internet provides a collaborative computational platform for participating enterprises, allowing the collection of big data for machine learning tasks. Despite the promise of training and deployment acceleration, and the potential to optimize decision-making processes through data-sharing, the adoption of such technologies is impacted by the increasing concerns about information privacy. As enterprises prefer to keep data private, this limits interoperability. While prior work has largely explored privacy-preserving mechanisms, the proposed methods naively average or randomly sample data shared from all participants instead of selecting the most well-suited subsets for a particular downstream learning task. Motivated by the lack of effective data-sharing mechanisms for heterogeneous machine learning tasks in Industrial Internet, we propose PriED, a task-driven data-sharing framework that selectively fuses shared data and local data from participants to improve supervised learning performance. PriED utilizes privacy-preserving data distillation to facilitate data exchange, and dynamic data selection to optimize downstream machine learning tasks. We demonstrate performance improvements on a real semiconductor manufacturing case study.