# RockNet: Distributed Learning on Ultra-Low-Power Devices

arXiv: https://arxiv.org/abs/2510.13320 · HTML: https://mlsanity.com/paper/2510.13320/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 97 references that are in the map.
- First posted on arXiv 15 Oct 2025.

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://mlsanity.com/paper/2004.09602/) (2020)
- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [Split Learning for collaborative deep learning in healthcare](https://mlsanity.com/paper/1912.12115/) (2019)
- [TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification](https://mlsanity.com/paper/1906.10329/) (2019)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Assaying Out-Of-Distribution Generalization in Transfer Learning](https://mlsanity.com/paper/2207.09239/) (2022)
- [ID and OOD Performance Are Sometimes Inversely Correlated on Real-world Datasets](https://mlsanity.com/paper/2209.00613/) (2022)
- [A TinyML Platform for On-Device Continual Learning with Quantized Latent Replays](https://mlsanity.com/paper/2110.10486/) (2021)
- [A Survey on Collaborative DNN Inference for Edge Intelligence](https://mlsanity.com/paper/2207.07812/) (2022)
- [Evaluation and Optimization of Distributed Machine Learning Techniques for Internet of Things](https://mlsanity.com/paper/2103.02762/) (2021)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey](https://mlsanity.com/paper/2310.10549/) (2023)
- [Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS](https://mlsanity.com/paper/2102.07244/) (2021)
- [Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients](https://mlsanity.com/paper/2402.11198/) (2024)
- [One Pixel Image and RF Signal Based Split Learning for mmWave Received Power Prediction](https://mlsanity.com/paper/1911.01682/) (2019)
- [TinyFedTL: Federated Transfer Learning on Tiny Devices](https://mlsanity.com/paper/2110.01107/) (2021)
- [SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices](https://mlsanity.com/paper/2006.04620/) (2020)
- [Synchronous Transmissions in Low-Power Wireless: A Survey of Communication Protocols and Network Services](https://mlsanity.com/paper/2001.08557/) (2020)
- [Wireless Control for Smart Manufacturing: Recent Approaches and Open Challenges](https://mlsanity.com/paper/2010.09087/) (2020)
- [Big Data Meet Cyber-Physical Systems: A Panoramic Survey](https://mlsanity.com/paper/1810.12399/) (2018)

## Built on by (most cited first, 1 of 1)

- [Going Beyond the Edge: Distributed Inference of Transformer Models on Ultra-Low-Power Wireless Devices](https://mlsanity.com/paper/2605.15694/) (2026)

## Abstract (arXiv)

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.