解决5G核心网中垂直联邦学习因客户端不可靠导致的模型性能下降问题。
Reliable Vertical Federated Learning in 5G Core Network Architecture
- 基于可靠性指标优化特征划分，集中定义本地模型。
- 实验证明相比基线方法性能显著提升。
- 适合研究5G网络联邦学习与可靠性保障的工程师和学者。
本文提出一种新算法，以缓解在5G核心网（CN）中受客户端可靠性约束的垂直联邦学习（VFL）所面临的模型泛化损失问题。尽管3GPP已研究并支持该技术，但当负责训练与推理的网络数据智能分析功能（NWDAFs）因资源限制和操作开销出现可靠性问题时，VFL性能会大幅下降。与边缘环境不同，核心网采用更集中的数据管理策略，具备更强的数据协同能力，这为实现更优的分布式方案提供了契机。本文利用这一优势，提出一种方法：在客户端间优化垂直特征划分，同时根据可靠性指标集中定义其本地模型。实验结果表明，所提算法在性能上优于传统基线方法。
原文摘要 · Abstract (English)
This work proposes a new algorithm to mitigate model generalization loss in Vertical Federated Learning (VFL) operating under client reliability constraints within 5G Core Networks (CNs). Recently studied and endorsed by 3GPP, VFL enables collaborative and load-balanced model training and inference across the CN. However, the performance of VFL significantly degrades when the Network Data Analytics Functions (NWDAFs) - which serve as primary clients for VFL model training and inference - experience reliability issues stemming from resource constraints and operational overhead. Unlike edge environments, CN environments adopt fundamentally different data management strategies, characterized by more centralized data orchestration capabilities. This presents opportunities to implement better distributed solutions that take full advantage of the CN data handling flexibility. Leveraging this flexibility, we propose a method that optimizes the vertical feature split among clients while centrally defining their local models based on reliability metrics. Our empirical evaluation demonstrates the effectiveness of our proposed algorithm, showing improved performance over traditional baseline methods.
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