用联邦学习实现6G网络智能传输，保护隐私还更高效
Federated Learning Based Decentralized Adaptive Intelligent Transmission Protocol for Privacy Preserving 6G Networks
- 基于联邦学习的去中心化协议，本地更新参数不传原始数据
- 实测比传统方法低延迟、高吞吐、省电，抗干扰更强
- 适合关注6G隐私安全与自适应传输的研究者和工程师
6G无线网络的发展带来隐私、可扩展性和适应性等新挑战。6G的数据密集特性难以由传统集中式网络模型有效处理，亟需向更安全、去中心化的系统转型。本文提出一种基于联邦学习的去中心化自适应智能传输协议（AITP），在去中心化架构中利用联邦学习的分布式训练能力，实现传输参数的实时智能调整。用户原始数据保留在本地边缘设备，保障隐私。通过数学建模与详细仿真评估，结果表明AITP在延迟、网络吞吐量、能效和鲁棒性等多项关键指标上均优于传统非自适应及集中式AI方法。该协议被视为未来6G网络的基础技术，支持以用户为中心、隐私优先的设计理念，推动6G隐私保护研究进展。
原文摘要 · Abstract (English)
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.
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