Journal
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2020)
Volume 180, Issue -, Pages 734-743Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2021.01.296
Keywords
machine learning; federated learning; collaborative learning; transfer learning; smart manufacturing
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Funding
- Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK)
- Federal Ministry for Digital and Economic Affairs (BMDW)
- Province of Upper Austria
- project AutoQual-I
- project PRIMAL
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The study discusses the limitations of federated machine learning frameworks in smart manufacturing and proposes a confidentiality-preserving approach based on the more general setting of transfer learning. The research aims to bridge the gap between current federated learning and requirements in industrial settings.
Federated machine learning frameworks, which take into account confidentiality of distributed data sources are of increasing interest in smart manufacturing. However, the scope of applicability of most such frameworks is restricted in industrial settings due to limitations in the assumptions on the data sources involved. In this work, first, we shed light on the nature of this arising gap between current federated learning and requirements in industrial settings. Our discussion aims at clarifying related notions in emerging sub-disciplines of machine learning, which are partially overlapping. Second, we envision a new confidentiality-preserving approach for smart manufacturing applications based on the more general setting of transfer learning, and envision its implementation in a module-based platform. (C) 2021 The Authors. Published by Elsevier B.V.
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