Decentralized Collaborative Learning with Probabilistic Data Protection

Ide, Tsuyoshi; Raymond, Rudy

doi:10.1109/smds53860.2021.00038

Decentralized Collaborative Learning with Probabilistic Data Protection

Conference · Wed Sep 01 04:00:00 EDT 2021 · 2021 IEEE International Conference on Smart Data Services (SMDS)

DOI:https://doi.org/10.1109/smds53860.2021.00038· OSTI ID:1958808

Ide, Tsuyoshi ^[1]; Raymond, Rudy

University of Nevada, Reno

We discuss future directions of Blockchain as a collaborative value co-creation platform, in which network participants can gain extra insights that cannot be accessed when disconnected from the others. As such, we propose a decentralized machine learning framework that is carefully designed to respect the values of democracy, diversity, and privacy. Specifically, we propose a federated multi-task learning framework that integrates a privacy-preserving dynamic consensus algorithm. We show that a specific network topology called the expander graph dramatically improves the scalability of global consensus building. We conclude the paper by making some remarks on open problems.

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Research Organization:: Nevada System of Higher Education

Sponsoring Organization:: USDOE Office of Electricity (OE)

DOE Contract Number:: OE0000911

OSTI ID:: 1958808

Conference Information:: Journal Name: 2021 IEEE International Conference on Smart Data Services (SMDS)

Country of Publication:: United States

Language:: English

References (21)

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