{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T23:42:59Z","timestamp":1769038979893,"version":"3.49.0"},"reference-count":91,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Cyber-Phys. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (TinyML), for example, due to privacy and latency concerns. However, CPS often comprise ultra-low-power microcontrollers, whose limited compute resources make training challenging. This article presents\n                    <jats:sc>RockNet<\/jats:sc>\n                    , a new TinyML method tailored for ultra-low-power hardware that achieves state-of-the-art accuracy in timeseries classification, such as fault or malware detection, without requiring offline pretraining. By leveraging that CPS consist of multiple devices, we design a distributed learning method that integrates ML and wireless communication.\n                    <jats:sc>RockNet<\/jats:sc>\n                    leverages all devices for distributed training of specialized compute efficient classifiers that need minimal communication overhead for parallelization. Combined with tailored and efficient wireless multi-hop communication protocols, our approach overcomes the communication bottleneck that often occurs in distributed learning. Hardware experiments on a testbed with 20 ultra-low-power devices demonstrate\n                    <jats:sc>RockNet<\/jats:sc>\n                    \u2019s effectiveness. It successfully learns timeseries classification tasks from scratch, surpassing the accuracy of the latest approach for neural network microcontroller training by up to 2x.\n                    <jats:sc>RockNet<\/jats:sc>\n                    \u2019s distributed ML architecture reduces memory, latency and energy consumption per device by up to 90% when scaling from one central device to 20 devices. Our results show that a tight integration of distributed ML, distributed computing, and communication enables, for the first time, training on ultra-low-power hardware with state-of-the-art accuracy.\n                  <\/jats:p>","DOI":"10.1145\/3773282","type":"journal-article","created":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T14:47:44Z","timestamp":1761576464000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>RockNet<\/scp>\n                    : Distributed Learning on Ultra-Low-Power Devices"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0120-0736","authenticated-orcid":false,"given":"Alexander","family":"Gr\u00e4fe","sequence":"first","affiliation":[{"name":"Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0468-0691","authenticated-orcid":false,"given":"Fabian","family":"Mager","sequence":"additional","affiliation":[{"name":"Networked Embedded Systems Lab, TU Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1450-2506","authenticated-orcid":false,"given":"Marco","family":"Zimmerling","sequence":"additional","affiliation":[{"name":"Networked Embedded Systems Lab, TU Darmstadt, Darmstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2785-2487","authenticated-orcid":false,"given":"Sebastian","family":"Trimpe","sequence":"additional","affiliation":[{"name":"Institute for Data Science in Mechanical Engineering, RWTH Aachen University, Aachen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"e_1_3_3_2_2","volume-title":"Control for societal-scale challenges: Road map 2030","author":"Annaswamy Anuradha M.","year":"2024","unstructured":"Anuradha M. 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