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

TL;DR

This paper introduces a federated learning-based decentralized transmission protocol for 6G networks that enhances privacy, scalability, and adaptability through real-time intelligent adjustments and robust performance improvements.

Contribution

It proposes a novel federated learning-based decentralized protocol for 6G, enabling privacy-preserving, adaptive transmission with superior performance over traditional methods.

Findings

Outperforms traditional non-adaptive methods in latency and throughput

Maintains user privacy by local data processing

Improves energy efficiency and robustness in 6G networks

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.