# Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction

arXiv: https://arxiv.org/abs/2104.08109 · HTML: https://mlsanity.com/paper/2104.08109/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Deep learning for universal linear embeddings of nonlinear dynamics](https://mlsanity.com/paper/1712.09707/) (2017)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems](https://mlsanity.com/paper/1708.06850/) (2017)
- [Wireless Network Intelligence at the Edge](https://mlsanity.com/paper/1812.02858/) (2018)
- [Deep Learning of Koopman Representation for Control](https://mlsanity.com/paper/2010.07546/) (2020)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach](https://mlsanity.com/paper/1905.04152/) (2019)
- [Extreme URLLC: Vision, Challenges, and Key Enablers](https://mlsanity.com/paper/2001.09683/) (2020)
- [Taming the Tail of Maximal Information Age in Wireless Industrial Networks](https://mlsanity.com/paper/1909.04335/) (2019)

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

- [Semantics-Native Communication with Contextual Reasoning](https://mlsanity.com/paper/2108.05681/) (2021)

## Abstract (arXiv)

Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.