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Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction
[article]
2021
arXiv
pre-print
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
arXiv:2104.08109v1
fatcat:ucla5onm4nedfgfiikvov55zne