The proliferation of IoT devices in smart cities challenges 6G networks with conflicting energy-latency requirements across heterogeneous slices. Existing approaches struggle with the energy-latency trade-off, particularly for massive scale deployments exceeding 50,000 devices km. This paper proposes an edge-aware CyberTwin framework integrating hybrid federated learning for energy-latency co-optimization in 6G network slicing. Our approach combines centralized Artificial Intelligence scheduling for latency-sensitive slices with distributed federated learning for non-critical slices, enhanced by compressive sensing-based digital twins and renewable energy-aware resource allocation. The hybrid scheduler leverages a three-tier architecture with Physical Unclonable Function (PUF) based security attestation achieving 99.7% attack detection accuracy. Comprehensive simulations demonstrate 52% energy reduction for non-real-time slices compared to Diffusion-Reinforcement Learning baselines while maintaining 0.9ms latency for URLLC applications with 99.1% SLA compliance. The framework scales to 50,000 devices km with CPU overhead below 25%, validated through NS-3 hybrid simulations across realistic smart city scenarios.

翻译：智慧城市中物联网设备的激增给6G网络带来了异构切片间能耗与延迟需求相互冲突的挑战。现有方法在能耗与延迟的权衡方面存在困难，尤其是在超过每平方公里5万台设备的大规模部署场景中。本文提出了一种边缘感知的数字孪生框架，该框架集成混合联邦学习以实现6G网络切片中的能耗-延迟协同优化。我们的方法将面向延迟敏感切片的集中式人工智能调度与面向非关键切片的分布式联邦学习相结合，并通过基于压缩感知的数字孪生及可再生能源感知的资源分配进行增强。混合调度器采用基于物理不可克隆功能（PUF）安全认证的三层架构，实现了99.7%的攻击检测准确率。综合仿真表明，与扩散-强化学习基线相比，非实时切片能耗降低52%，同时为超可靠低延迟通信（URLLC）应用保持0.9毫秒延迟，服务等级协议（SLA）合规率达99.1%。该框架可扩展至每平方公里5万台设备，CPU开销低于25%，并通过NS-3混合仿真在真实智慧城市场景中进行了验证。