In the modern and complex realm of networking, the pursuit of ideal QoS metrics is a fundamental objective aimed at maximizing network efficiency and user experiences. Nonetheless, the accomplishment of this task is hindered by the diversity of networks, the unpredictability of network conditions, and the rapid growth of multimedia traffic. This manuscript presents an innovative method for enhancing the QoS in SDN by combining the load-balancing capabilities of FL and genetic algorithms. The proposed solution aims to improve the dispersed aggregation of multimedia traffic by prioritizing data privacy and ensuring secure network load distribution. By using federated learning, multiple clients can collectively participate in the training process of a global model without compromising the privacy of their sensitive information. This method safeguards user privacy while facilitating the aggregation of distributed multimedia traffic. In addition, genetic algorithms are used to optimize network load balancing, thereby ensuring the efficient use of network resources and mitigating the risk of individual node overload. As a result of extensive testing, this research has demonstrated significant improvements in QoS measurements compared to traditional methods. Our proposed technique outperforms existing techniques such as RR, weighted RR, server load, LBBSRT, and dynamic server approaches in terms of CPU and memory utilization, as well as server requests across three testing servers. This novel methodology has applications in multiple industries, including telecommunications, multimedia streaming, and cloud computing. The proposed method presents an innovative strategy for addressing the optimization of QoS metrics in SDN environments, while preserving data privacy and optimizing network resource usage.

中文翻译：

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优化联邦学习中软件定义网络的 QoS 指标

在现代复杂的网络领域，追求理想的 QoS 指标是最大限度提高网络效率和用户体验的基本目标。然而，网络的多样性、网络条件的不可预测性以及多媒体流量的快速增长阻碍了这一任务的完成。本手稿提出了一种通过结合 FL 和遗传算法的负载均衡功能来增强 SDN 中 QoS 的创新方法。所提出的解决方案旨在通过优先考虑数据隐私并确保安全的网络负载分配来改善多媒体流量的分散聚合。通过使用联邦学习，多个客户端可以共同参与全局模型的训练过程，而不会损害其敏感信息的隐私。该方法保护用户隐私，同时促进分布式多媒体流量的聚合。此外，利用遗传算法优化网络负载均衡，从而保证网络资源的高效利用，降低单个节点过载的风险。经过大量测试，本研究表明与传统方法相比，QoS 测量有了显着改进。我们提出的技术在 CPU 和内存利用率以及跨三个测试服务器的服务器请求方面优于现有技术，例如 RR、加权 RR、服务器负载、LBBSRT 和动态服务器方法。这种新颖的方法可应用于多个行业，包括电信、多媒体流和云计算。