| Bibtype | Inproceedings |
|---|---|
| Bibkey | Heppe/etal/2020a |
| Author | Heppe, Lukas and Kamp, Michael and Adilova, Linara and Piatkowski, Nico and Heinrich, Danny and Morik, Katharina |
| Ls8autor |
Heinrich, Danny
Heppe, Lukas Morik, Katharina Piatkowski, Nico |
| Title | Resource-Constrained On-Device Learning by Dynamic Averaging |
| Booktitle | ECML PKDD 2020 Workshops |
| Pages | 129--144 |
| Address | Cham |
| Publisher | Springer International Publishing |
| Abstract | The communication between data-generating devices is par-
tially responsible for a growing portion of the world?s power consumption. Thus reducing communication is vital, both, from an economical and an ecological perspective. For machine learning, on-device learning avoids sending raw data, which can reduce communication substantially. Fur- thermore, not centralizing the data protects privacy-sensitive data. How- ever, most learning algorithms require hardware with high computation power and thus high energy consumption. In contrast, ultra-low-power processors, like FPGAs or micro-controllers, allow for energy-efficient learning of local models. Combined with communication-efficient dis- tributed learning strategies, this reduces the overall energy consumption and enables applications that were yet impossible due to limited energy on local devices. The major challenge is then, that the low-power pro- cessors typically only have integer processing capabilities. This paper investigates an approach to communication-efficient on-device learning of integer exponential families that can be executed on low-power pro- cessors, is privacy-preserving, and effectively minimizes communication. The empirical evaluation shows that the approach can reach a model quality comparable to a centrally learned regular model with an order of magnitude less communication. Comparing the overall energy consump- tion, this reduces the required energy for solving the machine learning task by a significant amount. |
| Year | 2020 |
| Projekt | SFB876-A1,SFB876-C3 |
| Isbn | 978-3-030-65965-3 |