## 7 Results and Analysis
### Energy Efficiency vs. Latency Tradeoffs  
As shown in Figure 11, our framework achieves **32% energy savings** with only **4.2% latency increase**, outperforming both FedAvg (25% savings at 9.1% latency) and centralized approaches (18% savings at 6.7% latency). This performance is significant, particularly in scenarios where latency constraints are critical, such as in Ultra-Reliable Low-Latency Communication (URLLC) systems, where the latency threshold must be maintained under stringent limits (a 5% increase in latency).  
In the literature, various techniques such as Federated Averaging (FedAvg) [8] and centralized approaches have been used to address energy efficiency and latency trade-offs in wireless communication systems. However, these techniques typically result in higher latency for the energy savings they provide. For example, FedAvg yields 25% energy savings but with a latency increase of 9.1%, significantly exceeding the 5% threshold required for URLLC applications. Our approach, by contrast, balances the energy savings and latency increase more effectively, ensuring system performance remains within the stringent latency limits.  
The LSTM-based dynamic scheduler integrated within our framework further optimizes the system performance by reducing the wake-up latency of the devices. As shown in Figure 12, the LSTM scheduler achieves a wake-up latency of **2.4 ms** (95% CI [2.1, 2.7]), which represents a **58% improvement** over traditional PID controllers [59]. This reduction in wake-up latency is crucial for enhancing the overall responsiveness of the system, particularly when the devices are operating in low-power modes and need to quickly respond to incoming tasks or data.  
Another key advantage of our framework is its ability to reduce training overhead. Our model converges to operational efficiency in **27% fewer training rounds** compared to baseline FedAvg. This reduction in training rounds directly translates to lower computational costs, making our approach more scalable and resource-efficient, particularly in large FL setups. Furthermore, CPU usage profiles show a peak reduction of **21%** during federated updates, highlighting the lower computational burden associated with our framework. The results demonstrate the potential of our approach in resource-constrained environments, where energy and latency are critical factors. By achieving a good balance between energy savings and latency, our framework can be applied in a variety of IoT and edge computing scenarios, particularly in healthcare, automotive, and industrial automation, where maintaining low latency and high energy efficiency are paramount. When comparing our results to those in the literature, the performance of our framework is notably superior. For instance, in the case of FedAvg [8], while it achieves energy savings, the latency increase is substantial, which can be detrimental in time-sensitive applications. On the other hand, our approach offers a compelling alternative that ensures the latency remains well below the critical threshold while still achieving significant energy savings. This positions our framework as a more effective solution for latency-sensitive and energy  
Figure 12: LSTM scheduler versus PID controller: wake-up latency reduction. The LSTM scheduler achieves significantly lower latency over training rounds, demonstrating superior efficiency in wake-up times compared to the PID controller baseline [59].  
efficient applications. Future improvements to the framework could include exploring other ML-based scheduling algorithms for further reduction in latency and computational overhead. Additionally, incorporating adaptive scheduling based on real-time network conditions could provide further benefits in dynamic and unpredictable environments.