The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
On-Device Training of Machine Learning Models on Microcontrollers With a Look at Federated Learning
Open accessAbstract
Recent progress in machine learning frameworks makes it now possible to run an inference with sophisticated machine learning models on tiny microcontrollers. Model training, however, is typically done separately on powerful computers. There, the training process has abundant CPU and memory resources to process the stored datasets. In this work, we explore a different approach: training the model directly on the microcontroller. We implement this approach for a keyword spotting task. Then, we extend the training process using federated learning among microcontrollers. Our experiments with model training show an overall trend of decreasing loss with the increase of training epochs.
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In the citing paper
“Research shows on-device training for TinyML domain with intervention from the federated learning (Grau et al., 2021).”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Grau et al., 2021
In the citing paper
“It implements the key word spotting application by using an Arduino Nano 33 BLE Sensor board.”
From A review on TinyML: State-of-the-art and prospects (2022) · cites this paper as Grau et al., 2021
Citations
Cites
The emergence of edge computing
Satyanarayanan, M. (2017) · Computer · doi:10.1109/mc.2017.9 · Cited by 2,832
Machine Learning on Mainstream Microcontrollers
Sakr, F., Bellotti, F., Berta, R., & De Gloria, A. (2020) · Sensors · doi:10.3390/s20092638 · Cited by 109
Research works printed but not linked: 7
- Colby R. Banbury Vijay Janapa Reddi Max Lam William Fu Amin Fazel Jeremy Holleman Xinyuan Huang Robert Hurtado David Kanter Anton Lokhmotov David Patterson Danilo Pau Jae sun Seo Jeff Sieracki Urmish Thakker Marian Verhelst and Poonam Yadav. 2021. Benchmarking TinyML Systems: Challenges and Direction. arXiv:2003.04821 [cs.PF] Colby R. Banbury Vijay Janapa Reddi Max Lam William Fu Amin Fazel Jeremy Holleman Xinyuan Huang Robert Hurtado David Kanter Anton Lokhmotov David Patterson Danilo Pau Jae sun Seo Jeff Sieracki Urmish Thakker Marian Verhelst and Poonam Yadav. 2021. Benchmarking TinyML Systems: Challenges and Direction. arXiv:2003.04821 [cs.PF] · arXiv
- Olivia Choudhury Aris Divanis Theodoros Salonidis Issa Sylla Yoonyoung Park Grace Hsu and Amar Das. 2020. Differential Privacy-enabled Federated Learning for Sensitive Health Data. arXiv:1910.02578 [cs.LG] Olivia Choudhury Aris Divanis Theodoros Salonidis Issa Sylla Yoonyoung Park Grace Hsu and Amar Das. 2020. Differential Privacy-enabled Federated Learning for Sensitive Health Data. arXiv:1910.02578 [cs.LG] · arXiv
- Yuang Jiang , Shiqiang Wang , Victor Valls , Bong Jun Ko , Wei-Han Lee, Kin K. Leung, and Leandros Tassiulas. 2020 . Model Pruning Enables Efficient Federated Learning on Edge Devices . arXiv:1909.12326 [cs.LG] Yuang Jiang, Shiqiang Wang, Victor Valls, Bong Jun Ko, Wei-Han Lee, Kin K. Leung, and Leandros Tassiulas. 2020. Model Pruning Enables Efficient Federated Learning on Edge Devices. arXiv:1909.12326 [cs.LG] · arXiv
- Ashish Kumar , Saurabh Goyal , and Manik Varma . 2017 . Resource-efficient Machine Learning in 2 kB RAM for the Internet of Things . In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, http://proceedings.mlr.press/v70/kumar17a.html, 1935-- 1944 . http://proceedings.mlr.press/v70/kumar17a.html Ashish Kumar, Saurabh Goyal, and Manik Varma. 2017. Resource-efficient Machine Learning in 2 kB RAM for the Internet of Things. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, http://proceedings.mlr.press/v70/kumar17a.html, 1935--1944. http://proceedings.mlr.press/v70/kumar17a.html
- Haoyu Ren Darko Anicic and Thomas Runkler. 2021. TinyOL: TinyML with Online-Learning on Microcontrollers. arXiv:2103.08295 [cs.LG] Haoyu Ren Darko Anicic and Thomas Runkler. 2021. TinyOL: TinyML with Online-Learning on Microcontrollers. arXiv:2103.08295 [cs.LG] · DOI · Paper page
- 10.1109/MC.2017.4451215 · DOI
- Qiang Yang , Yang Liu , Tianjian Chen , and Yongxin Tong . 2019 . Federated Machine Learning : Concept and Applications. ACM Trans. Intell. Syst. Technol. 10 , 2, Article 12 (Jan. 2019), 19 pages. https://doi.org/10.1145/3298981 Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. 2019. Federated Machine Learning: Concept and Applications. ACM Trans. Intell. Syst. Technol. 10, 2, Article 12 (Jan. 2019), 19 pages. https://doi.org/10.1145/3298981 · DOI · Paper page
Cited by
A review on TinyML: State-of-the-art and prospects
Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460
“Research shows on-device training for TinyML domain with intervention from the federated learning (Grau et al., 2021).”
Metadata
- Authors
- Grau, M. M., Centelles, R. P., & Freitag, F.
- Affiliations
- Universitat Politècnica de Catalunya (ES)
- Technical University of Catalonia, Barcelona, Spain
- Venue
- Proceedings of the Conference on Information Technology for Social Good
- Publisher
- ACM
- Published
- 2021-09-09
- Pages
- 198-203
- OpenAlex
- W3196077236
- Cited by
- 28
- References
- 2
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 8 · 2023: 7 · 2022: 6 · 2021: 1
- FWCI
- 2.54
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- International Conference on Information Technology for Social Good (GoodIT) (publishedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
- Funders
- Spanish State Agency for Research (PID2019-106774RB-C21)
- CHIST-ERA (PCI2019-111850-2)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- on-device learning, microcontroller, federated learning, keyword spotting, edge computing, resource-constrained devices
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