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

2025/12/20 by Ansar Ahmed, Ahmed, Ansar
Computer Science · Engineering · #Advanced Wireless Communication Technologies #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #IoT and Edge/Fog Computing #Privacy-Preserving Technologies in Data

paper · doi:10.48550/arxiv.2512.18432

openalex publication_date 2025/12/20 · openalex created_date 2025/12/24 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Federated Edge Learning for Predictive Maintenance in 6G Small Cell Networks
• Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications
• DFedSat: Communication-Efficient and Robust Decentralized Federated Learning for LEO Satellite Constellations
• Snake Learning: A Communication- and Computation-Efficient Distributed Learning Framework for 6G
• Addressing Privacy Concerns in Joint Communication and Sensing for 6G Networks: Challenges and Prospects
• Federated Analytics for 6G Networks: Applications, Challenges, and Opportunities
• Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey
• Split Federated Learning for 6G Enabled-Networks: Requirements, Challenges and Future Directions
• Advancing Federated Learning in 6G: A Trusted Architecture with Graph-based Analysis
• Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing
• Decentralized Federated Learning: A Survey and Perspective
• Optimization Design for Federated Learning in Heterogeneous 6G Networks
• Toward a Smart Resource Allocation Policy via Artificial Intelligence in 6G Networks: Centralized or Decentralized?
• Federated Learning for 6G: Applications, Challenges, and Opportunities

Related

• Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights
• Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Control
• Decentralized Wireless Federated Learning with Differential Privacy
• Optimization Design for Federated Learning in Heterogeneous 6G Networks
• Edge-Native Intelligence for 6G Communications Driven by Federated Learning: A Survey of Trends and Challenges