DT-Block:面向 6G 安全高效通信的自适应垂直联合强化学习方案

IF 4.4 2区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
Ihsan H. Abdulqadder , Israa T. Aziz , Deqing Zou
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引用次数: 0

摘要

由于使用分散的数据源,安全和数据共享的必要性已成为联合学习的重点。现有的研究将联合学习用于安全方面,但它仍然面临许多挑战,如安全性和隐私性差、计算复杂性等。在这项研究中,我们利用强化学习方法和区块链提出了自适应垂直联合学习。拟议的工作包括三个阶段:用户注册和身份验证、基于机器学习的客户端选择和自适应安全联合学习。首先,所有用户向认知代理注册其证书,认知代理使用混沌等源后量子加密(CIPQC)算法生成私钥、公钥和随机数。其次,选择最佳客户端参与联合学习,从而提高学习率并降低复杂性。在这里,增强型多层前馈神经网络(EMFFN)算法通过考虑 CSI、RSSI、带宽、能量、通信效率和统计效率来选择最佳客户端。最后,通过分布式深度确定性策略梯度(D4PG)算法进行自适应安全联合学习,其中局部模型由私有策略根据其灵敏度自适应使用。聚合的全局模型存储在 DT-block(基于树状结构的区块链)中,该区块链以树状结构存储数据,以提高可扩展性并减少数据检索时的搜索时间。这项研究通过 NS-3.26 网络模拟器进行了模拟,并根据各种性能指标(如准确率、延迟、损失、f1-分数和安全强度)对所建议的 DT-Block 模型的性能进行了评估。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DT-Block: Adaptive vertical federated reinforcement learning scheme for secure and efficient communication in 6G
The necessities of security and data sharing have focused on federated learning because of using decentralized data sources. The existing works used federated learning for security, however, it still faces many challenges such as poor security and privacy, computational complexity, etc. In this research, we propose adaptive vertical federated learning using a reinforcement learning approach and blockchain. The proposed work includes three phases: user registration and authentication, machine learning-based client selection, and adaptive secure federated learning. Initially, all the users register their credentials to the cognitive agent, which generates a private key, public key, and random number using a Chaotic Isogenic Post Quantum Cryptography (CIPQC) algorithm. Second, optimal clients are selected for participating in federated learning which improves learning rate and reduces complexity. Here, optimal clients are selected by the Enhanced Multilayer Feed Forward Neural Network (EMFFN) algorithm by considering CSI, RSSI, bandwidth, energy, communication efficiency, and statistical efficiency. Finally, adaptive secure federated learning is performed by the Distributed Distributional Deep Deterministic Policy Gradient (D4PG) algorithm, where the local models are adaptively used by the private strategy based on its sensitivity. The aggregated global models are stored in DT-block (dendrimer tree-based blockchain) which stores the data in a dendrimer tree structure for increasing scalability and reducing search time during data retrieval. The simulation of this research is conducted by NS-3.26 network simulator and the performance of the proposed DT-Block model is estimated based on various performance metrics such as accuracy, delay, loss, f1-score, and security strength this demonstrated that the suggested effort produced better results both in terms of quantitative and qualitative aspects.
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来源期刊
Computer Networks
Computer Networks 工程技术-电信学
CiteScore
10.80
自引率
3.60%
发文量
434
审稿时长
8.6 months
期刊介绍: Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.
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