## 7 Results and Analysis
### Comparison with Advanced Baselines  
To validate the competitiveness of our proposed framework, we conducted a comprehensive comparison against two advanced baselines that represent state-of-the-art approaches in FL and secure model aggregation. The first baseline is a Transformer-based FL approach, where a Transformer model is trained on the DAWN dataset specifically for traffic prediction tasks. This model leverages self-attention layers to enhance temporal feature extraction, enabling the system to capture long-term dependencies inherent in sequential data streams, as detailed by [PERSON] et al. [64]. Given its ability to model complex temporal patterns, this architecture is particularly appealing for dynamic Distributed Unit (DU) sleep scheduling in scenarios with fluctuating network loads. The second \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Metric** & **Our Framework** & **DP-FL Baseline**[61] & **Requirement** \\ \hline Accuracy @ \(\epsilon=2.0\) & 92\% & 88\% & — \\ \hline MI Success Rate @ \(\epsilon=2.0\) & 12.3\% & 31.7\% & \textless{}15\% \\ \hline Additional Latency per FL Round & 3.8 ms & 5.2 ms & \textless{}10 ms \\ \hline Additional Computation Overhead & +8\% & +12\% & \textless{}15\% \\ \hline GDPR Compliance & Yes (Rénié Accountant) & Yes (Laplace mechanism) & Art. 29 WP29 Guidance \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Privacy mechanism overhead and efficacy. Figure 17: Membership-inference attack success: our adaptive DP reduces attack success to 12.3% at \(\epsilon=2.0\) versus 31.7% for DP-FL [61]. baseline involves a Blockchain-enhanced FL framework, which incorporates Blockchain-based secure aggregation techniques, as proposed by [PERSON] et al. [43]. In this approach, model updates are transmitted and verified through smart contracts operating on a private Ethereum testnet. This method ensures the tamper-proof aggregation of local models and significantly enhances accountability and transparency by leveraging the decentralized and immutable nature of Blockchain technology. The inherent security guarantees make this approach attractive in adversarial environments where data poisoning and insider threats are prevalent. In our experimental evaluation, the task focuses on dynamic DU sleep scheduling under adversarial conditions, simulating an environment with 15% poisoning attackers attempting to compromise model integrity. The key performance metrics considered include energy savings, latency--both mean and 99 th percentile--and poisoning resilience, which quantifies the framework's robustness against the injected adversarial attacks. From the results presented in Table 14, several critical observations emerge. The Transformer-FL approach achieves a respectable poisoning resilience of 82%, significantly outperforming conventional FedAvg and centralized schemes in terms of security. However, this comes at the cost of increased latency, registering 2.5 milliseconds, which is more than twice the latency of our framework. This elevated latency can be attributed to the computational overhead associated with the self-attention mechanisms, whose quadratic complexity in sequence length makes them inefficient for real-time applications such as RAN optimization. Although Transformer models excel in tasks like traffic classification, where temporal precision is less stringent, their applicability to latency-sensitive scenarios is limited. In contrast, the Blockchain-FL baseline demonstrates almost perfect poisoning resilience, reaching 99%, highlighting its strength in mitigating adversarial threats through robust consensus and cryptographic verification. Nevertheless, these security enhancements introduce a substantial latency penalty, with the system exhibiting a latency of 15.3 milliseconds--over 12 times higher than our proposed approach. Such a delay is incompatible with the strict timing requirements of Near-Real-Time Radio Intelligent Controller (RIC) workflows, which typically operate under 10-millisecond control loops.