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

arXiv 2512.18432 ↗First posted 20 Dec 2025cs.CR cs.AIcited by 0

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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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 70 references that are in the map. First posted on arXiv 20 Dec 2025.

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References · 16

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2020Federated Learning for 6G Communications: Challenges, Methods, and Future Directionscited by 32
  3. 2020Low-latency Federated Learning and Blockchain for Edge Association in Digital Twin empowered 6G Networkscited by 30
  4. 2022Five Facets of 6G: Research Challenges and Opportunitiescited by 17
  5. 2023Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directionscited by 8
  6. 2023Optimization Design for Federated Learning in Heterogeneous 6G Networkscited by 7
  7. 2023Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysiscited by 6
  8. 2024DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellationscited by 6
  9. 2023Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Surveycited by 3
  10. 2024Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6Gcited by 3
  11. 2024Addressing Privacy Concerns in Joint Communication and Sensing for 6G Networks: Challenges and Prospectscited by 2
  12. 2022Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?cited by 1
4 more
  1. 2023Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicingcited by 1
  2. 2024Federated Analytics for 6G Networks: Applications, Challenges, and Opportunitiescited by 1
  3. 2025Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applicationscited by 1
  4. 2025Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networkscited by 1

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