# Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks

arXiv: https://arxiv.org/abs/2512.18432 · HTML: https://mlsanity.com/paper/2512.18432/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 70 references that are in the map.
- First posted on arXiv 20 Dec 2025.

## Builds on (most cited first, 16 of 16)

- [Decentralized Federated Learning: A Survey and Perspective](https://mlsanity.com/paper/2306.01603/) (2023)
- [Federated Learning for 6G Communications: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/2006.02931/) (2020)
- [Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networks](https://mlsanity.com/paper/2011.09902/) (2020)
- [Five Facets of 6G: Research Challenges and Opportunities](https://mlsanity.com/paper/2212.07902/) (2022)
- [Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions](https://mlsanity.com/paper/2309.09086/) (2023)
- [Optimization Design for Federated Learning in Heterogeneous 6G Networks](https://mlsanity.com/paper/2303.08322/) (2023)
- [Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis](https://mlsanity.com/paper/2309.05525/) (2023)
- [DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellations](https://mlsanity.com/paper/2407.05850/) (2024)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G](https://mlsanity.com/paper/2405.03372/) (2024)
- [Addressing Privacy Concerns in Joint Communication and Sensing for 6G Networks: Challenges and Prospects](https://mlsanity.com/paper/2405.01742/) (2024)
- [Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?](https://mlsanity.com/paper/2202.09093/) (2022)
- [Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing](https://mlsanity.com/paper/2307.09494/) (2023)
- [Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities](https://mlsanity.com/paper/2401.03878/) (2024)
- [Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications](https://mlsanity.com/paper/2508.08479/) (2025)
- [Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networks](https://mlsanity.com/paper/2509.11421/) (2025)

## Abstract (arXiv)

The move to 6th Generation (6G) wireless networks creates new issues with privacy, scalability, and adaptability. The data-intensive nature of 6G is not handled well by older, centralized network models. A shift toward more secure and decentralized systems is therefore required. A new framework called the Federated Learning-based Decentralized Adaptive Intelligent Transmission Protocol (AITP) is proposed to meet these challenges. The AITP uses the distributed learning of Federated Learning (FL) within a decentralized system. Transmission parameters can be adjusted intelligently in real time. User privacy is maintained by keeping raw data on local edge devices. The protocol's performance was evaluated with mathematical modeling and detailed simulations. It was shown to be superior to traditional non-adaptive and centralized AI methods across several key metrics. These included latency, network throughput, energy efficiency, and robustness. The AITP is presented as a foundational technology for future 6G networks that supports a user-centric, privacy-first design. This study is a step forward for privacy-preserving research in 6G.