## 5 Proposed Framework
### FL Framework
#### 5.2.4 Implementation and Validation
For the scheduler module, an LSTM-based model with 128 hidden units and a dropout rate of 0.2 was used, operating on 10-step historical sequences representing 5-min windows. The optimizer was AdamW with a weight decay parameter set to \(\lambda=0.001\). The zero-trust authentication module implemented token refreshing at 50 ms intervals using ECDSA-P384 cryptographic signatures. Anomaly detection thresholds were dynamically set to \(\lambda_{thresh}=\mu+3\sigma\), where \(\mu\) and \(\sigma\) represent the mean and standard deviation of observed authentication events. In terms of Kubernetes orchestration policies, Calico CNI was employed with strict micro-segmentation rules isolating the Core, Controlled, and Quarantine Zones. Resource limits were enforced at the application level, with each xApp restricted to four virtual CPUs, 16 GB of RAM, and one GPU per instance, ensuring consistent resource fairness and predictability across the testbed. Experimental Validation  
The framework was evaluated under three scenarios, and the learning rate followed an adaptive decay schedule, initialized at \(\eta=0.01\) and reduced by 50% every 50 rounds to balance convergence speed and stability. The validation scenarios, including participant configurations and key evaluation metrics under normal, adversarial, and extreme conditions, are summarized in Table 9.  
* **Energy Efficiency**:
* Achieved 32.4% \(\pm\) 1.2% improvement across slices. * eMBB: 34.1% savings (95% CI [32.8, 35.4]). * URLLC: 28.7% savings (95% CI [27.2, 30.2]). * **Latency Impact**:
* Baseline: 12.4 ms \(\pm\) 0.8 ms.
* Secure FL: 12.9 ms \(\pm\) 1.1 ms (+4.2%). * 99 th percentile: 15.2 ms (meets O-RAN Near-RT requirements). * **Privacy Guarantees**:
* (\(\epsilon=2.0,\delta=10^{-5}\)) via Renyi Accountant. * Model inversion attack success rate: 2.3% (vs 89% non-private). Adversarial Resilience  
Under attack conditions (20% malicious participants). The accuracy under poisoning attacks is illustrated in Figure 8:  
\begin{table} \begin{tabular}{l l l} \hline \hline
**Scenario** & **Participants and Attackers** & **Key Evaluation Metric** \\ \hline Normal Operation & 100 participants, 0 attackers & Energy Efficiency \\ \hline Adversarial & 100 participants, 15–20 attackers & Model Accuracy \\ \hline Extreme Case & 100 participants, 30 attackers & Convergence Rate \\ \hline \hline \end{tabular}   
* **Energy Efficiency**:
* Achieved 32.4% \(\pm\) 1.2% improvement across slices. * eMBB: 34.1% savings (95% CI [32.8, 35.4]). * URLLC: 28.7% savings (95% CI [27.2, 30.2]). * **Latency Impact**:
* Baseline: 12.4 ms \(\pm\) 0.8 ms.
* Secure FL: 12.9 ms \(\pm\) 1.1 ms (+4.2%). * 99 th percentile: 15.2 ms (meets O-RAN Near-RT requirements). * **Privacy Guarantees**:
* (\(\epsilon=2.0,\delta=10^{-5}\)) via Renyi Accountant. * Model inversion attack success rate: 2.3% (vs 89% non-private). Adversarial Resilience  
Under attack conditions (20% malicious participants). The accuracy under poisoning attacks is illustrated in Figure 8:  
\begin{table} \begin{tabular}{l l l} \hline \hline
**Scenario** & **Participants and Attackers** & **Key Evaluation Metric** \\ \hline Normal Operation & 100 participants, 0 attackers & Energy Efficiency \\ \hline Adversarial & 100 participants, 15–20 attackers & Model Accuracy \\ \hline Extreme Case & 100 participants, 30 attackers & Convergence Rate \\ \hline \hline \end{tabular}   
* **Energy Efficiency**:
* Achieved 32.4% \(\pm\) 1.2% improvement across slices. * eMBB: 34.1% savings (95% CI [32.8, 35.4]). * URLLC: 28.7% savings (95% CI [27.2, 30.2]). * **Latency Impact**:
* Baseline: 12.4 ms \(\pm\) 0.8 ms.
* Secure FL: 12.9 ms \(\pm\) 1.1 ms (+4.2%). * 99 th percentile: 15.2 ms (meets O-RAN Near-RT requirements). * **Privacy Guarantees**:
* (\(\epsilon=2.0,\delta=10^{-5}\)) via Renyi Accountant. * Model inversion attack success rate: 2.3% (vs 89% non-private). Adversarial Resilience  
Under attack conditions (20% malicious participants). The accuracy under poisoning attacks is illustrated in Figure 8:  
\begin{table}