# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 5 Simulations
### Results  
The following figures compare the performance of various client selection strategies: MACS, Static, Random, Reinforcement Learning-based FL (RL-based), and Deep Learning-based FL (DL-based). These key metrics, number of selected clients, average data rate, computational capacity, delay, and coverage, are analyzed to evaluate MACS's efficiency in dynamic IoT environments. Figure 2 illustrates the number of clients selected in each iteration. MACS invariably balances client selection, ensuring stable participation and minimizing client dropouts. In contrast, RL-based and DL-based methods select more clients but often result in computational inefficiencies and increased network load. The Static selection method struggles with fluctuating client availability due to its fixed approach, while Random Selection leads to unpredictable participation, negatively impacting convergence. MACS maintains an optimal selection strategy, avoiding unnecessary delays while maximizing the number of contributing clients. Figure 3 shows that MACS maintains the highest and most stable average data rate by selecting clients with reliable network conditions. RL-based and DL-based exhibit fluctuations in the data rate due to their reliance on continuous model adjustments, which do not always prioritize network stability. Static selection leads to moderate performance but lacks adaptability, while random selection results in erratic communication quality, introducing potential delays. MACS's ability to filter clients based on network stability ensures faster and more consistent model updates, minimizing communication bottlenecks. Figure 4 reveals that MACS prioritizes clients with sufficient computational resources, ensuring efficient model updates while preventing overload on weaker devices. While DL-based selects clients with high computational power, its increased selection size can lead to inefficiencies. RL-based dynamically selects clients but does not always ensure consistent computational availability. Static and Random selection suffer from inconsistent computational capacity, often leading to increased delays and slower training convergence. MACS optimizes selection to provide an ideal balance between computational efficiency and resource utilization. Figure 5 indicates that MACS achieves the lowest delay across all selection methods. By proactively selecting clients with \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Background noise \(N_{0}\) & \(-94\) dBm \\ Bandwidth (\(b\)) & \(1\) MHz \\ Client transmission power (\(p\)) & \(0.1\) watt \\ Size of the local model (\(s\)) & \(100\) kbit \\ Client \(u\) CPU frequency & \(f_{u}\in U(0.1,1)\times 10^{9}\) Hz \\ Number of CPU cycles required for & \(C_{u}\in U(1,5)\times 10^{7}\) \\ training one sample on client \(u\) & \\ Number of local samples \(|\mathcal{D}_{u}|\) & Dirichlet distribution \\ Number of local epochs & Various, dynamic local training \\ Number of local batch size & \(10\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. stable mobility and high computational capabilities, MACS minimizes interruptions in training and ensures timely model updates. RL-based and DL-based show lower delays than Static and Random selection but still experience fluctuations due to their selection unpredictability. Static selection suffers from higher delays as it does not account for changing network conditions, while Random selection leads to unpredictable delays due to the inclusion of suboptimal clients. The results demonstrate MACS's effectiveness in reducing training time and improving overall FL efficiency. Figure 6 evaluates the indicator's ability to maintain consistent client connectivity throughout training. MACS achieves the highest coverage by integrating a mobility prediction model that ensures clients remain available for the duration of the training rounds.