## 8 Conclusions  
In the face of increasingly complex and dynamic environments, securing Open Radio Access Networks (O-RANs) is critical to ensuring reliable, scalable, and efficient 5G and future 6G services. With the growing dependence on FL and multi-operator systems, security, privacy, and performance concerns become more pronounced. The existing challenges, including energy-efficiency trade-offs, vulnerabilities to poisoning attacks, and privacy breaches in federated edge intelligence systems, highlight the need for more robust and efficient security frameworks. This work proposes a novel framework that integrates ZTA, energy-latency optimization, and privacy-preserving mechanisms for O-RAN optimization, addressing these challenges in a comprehensive manner. Our approach provides several key innovations and improvements in O-RAN security and performance:  
* We demonstrate a 32% energy reduction while maintaining a latency increase of <5%, ensuring compliance with Ultra-Reliable Low Latency Communication (URLLC) requirements. The framework achieves a 99 th percentile latency [66] of 1.2 ms, closely approaching the <1 ms target for critical applications. * The ZTA architecture significantly enhances security by reducing **89%** of access control breaches, while its anomaly detection capabilities offer **16%** better coverage compared to state-of-the-art (SOTA) detectors. \begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Result** \\ \hline Energy Savings & 32\% \\ Latency Increase & 4.2\% (below 5\% URLLC threshold) \\ DU Wake-Up Latency Reduction & 58\% vs. PID \\ Training Efficiency & 27\% fewer rounds than FedAvg \\ Attack Success Rate & 11\% (versus 87\% RBAC, 100\% none) \\ Poisoning Resilience (15\% adversaries) & 94\% global accuracy \\ Access Control Breach Reduction & 89\% \\ Anomaly Detection F1-Score & 94\% \\ Membership Inference Success Rate & 12.3\% \\ Privacy Overhead per FL Round & 3.8 ms \\ Computation Overhead & +8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Summary of Key Performance Metrics. * Our adaptive differential privacy (DP) mechanism preserves **92% accuracy at \(\epsilon=2\)**, compared to the baseline's **88%** while reducing inference risks by **60%**, thereby ensuring GDPR compliance for real-world deployment. These findings highlight the potential of our framework to transform O-RAN systems by providing a strong security posture, efficient energy management, and enhanced privacy protection without compromising performance. While prior comparisons focused on FedAvg and centralized baselines, our extended evaluation demonstrates that the proposed framework achieves competitive energy efficiency (32% savings) and sub-millisecond latency (1.2 ms) compared to modern approaches like Transformer-FL and Blockchain-FL. Notably, our method uniquely balances these metrics while maintaining 94% poisoning resilience under adversarial conditions, making it practical for real-world O-RAN deployments. The proposed framework addresses critical security, privacy, and performance gaps in current O-RAN designs. By integrating predictive energy optimization, our framework provides energy-efficient solutions that are crucial for the sustainability of next-generation networks. Furthermore, the application of Zero Trust models ensures that even if some elements are compromised, the network maintains a secure operation, minimizing the impact of potential breaches. The privacy-preserving mechanisms, particularly the adaptive differential privacy strategy, present a significant advancement in FL environments, allowing for enhanced model training without sacrificing data privacy. This enables practical deployment in sectors such as healthcare and finance, where confidentiality and security are paramount. While our framework offers significant improvements, it is not without limitations.