RockNet: Distributed Learning on Ultra-Low-Power Devices

• URL: http://arxiv.org/abs/2510.13320v2
• Date: Fri, 24 Oct 2025 07:52:31 GMT
• Title: RockNet: Distributed Learning on Ultra-Low-Power Devices
• Authors: Alexander Gräfe, Fabian Mager, Marco Zimmerling, Sebastian Trimpe,
• Abstract summary: This paper presents RockNet, a new TinyML method tailored for ultra-low-power hardware.<n>By leveraging that CPS consist of multiple devices, we design a distributed learning method that integrates Machine Learning and wireless communication.<n>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.
• Score: 49.01692357536576
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 paper presents RockNet, 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. RockNet 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 RockNet's 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. RockNet's 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.

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