# Massive Twinning to Enhance Emergent Intelligence

arXiv: https://arxiv.org/abs/2204.09316 · HTML: https://mlsanity.com/paper/2204.09316/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 19 references that are in the map.
- First posted on arXiv 20 Apr 2022.

## Builds on (most cited first, 3 of 3)

- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)
- [D2D-Based Grouped Random Access to Mitigate Mobile Access Congestion in 5G Sensor Networks](https://mlsanity.com/paper/1705.02777/) (2017)

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

- [A Survey on Agentic Service Ecosystems: Measurement, Analysis, and Optimization](https://mlsanity.com/paper/2508.07343/) (2025)
- [Trust-Awareness to Secure Swarm Intelligence from Data Injection Attack](https://mlsanity.com/paper/2211.08407/) (2022)

## Abstract (arXiv)

As a complement to conventional AI solutions, emergent intelligence (EI) exhibits competitiveness in 6G IIoT scenario for its various outstanding features including robustness, protection to privacy, and scalability. However, despite the low computational complexity, EI is challenged by its high demand of data traffic in massive deployment. We propose to leverage massive twinning, which 6G is envisaged to support, to reduce the data traffic in EI and therewith enhance its performance.