# Neuromorphic Event-Driven Semantic Communication in Microgrids

arXiv: https://arxiv.org/abs/2402.18390 · HTML: https://mlsanity.com/paper/2402.18390/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 28 Feb 2024.

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

- [BinaryConnect: Training Deep Neural Networks with binary weights during propagations](https://mlsanity.com/paper/1511.00363/) (2015)
- [Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications](https://mlsanity.com/paper/2207.09353/) (2022)
- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [An Introduction to Probabilistic Spiking Neural Networks: Probabilistic Models, Learning Rules, and Applications](https://mlsanity.com/paper/1910.01059/) (2019)
- [Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference](https://mlsanity.com/paper/2206.06047/) (2022)
- [Self-Healing Secure Blockchain Framework in Microgrids](https://mlsanity.com/paper/2209.07138/) (2022)
- [Delay-Aware Semantic Sampling in Power Electronic Systems](https://mlsanity.com/paper/2402.13586/) (2024)
- [A Monolithic Cybersecurity Architecture for Power Electronic Systems](https://mlsanity.com/paper/2402.13617/) (2024)
- [A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow](https://mlsanity.com/paper/2002.11843/) (2020)

## Built on by (most cited first, 2 of 2)

- [Inferring Ingrained Remote Information in AC Power Flows Using Neuromorphic Modality Regime](https://mlsanity.com/paper/2407.14883/) (2024)
- [Spike Talk in Power Electronic Grids -- Leveraging Post Moore's Computing Laws](https://mlsanity.com/paper/2411.07654/) (2024)

## Abstract (arXiv)

Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids. On one hand, coordination among sources is facilitated by distributed, privacy-minded processing at multiple locations, whereas on the other hand, it also creates exogenous data arrival paths for adversaries that can lead to cyber-physical attacks amongst other reliability issues in the communication layer. This long-standing problem necessitates new intrinsic ways of exchanging information between converters through power lines to optimize the system's control performance. Going beyond the existing power and data co-transfer technologies that are limited by efficiency and scalability concerns, this paper proposes neuromorphic learning to implant communicative features using spiking neural networks (SNNs) at each node, which is trained collaboratively in an online manner simply using the power exchanges between the nodes. As opposed to the conventional neuromorphic sensors that operate with spiking signals, we employ an event-driven selective process to collect sparse data for training of SNNs. Finally, its multi-fold effectiveness and reliable performance is validated under simulation conditions with different microgrid topologies and components to establish a new direction in the sense-actuate-compute cycle for power electronic dominated grids and microgrids.