Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.procs.2021.01.296 | PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING (ISM 2020) |
Keywords | DocType | Volume |
machine learning, federated learning, collaborative learning, transfer learning, smart manufacturing | Conference | 180 |
ISSN | Citations | PageRank |
1877-0509 | 0 | 0.34 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Werner Zellinger | 1 | 32 | 4.27 |
| Volkmar Wieser | 2 | 0 | 0.34 |
| Mohit Kumar | 3 | 0 | 0.34 |
| David Brunner | 4 | 0 | 0.34 |
| Natalia Shepeleva | 5 | 0 | 0.34 |
| Rafa Galvez | 6 | 0 | 0.34 |
| Josef Langer | 7 | 0 | 0.34 |
| Lukas Fischer | 8 | 0 | 0.34 |
| Bernhard Moser | 9 | 0 | 0.34 |