# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 5 Simulations
### Accuracy Evaluation on CIFAR and MNIST Datasets  
The Mobility-Aware Client Selection (MACS) strategy was evaluated against Static client selection, Random selection, Reinforcement Learning-based FL (RL-based), and Deep Learning-based FL (DL-based) using the CIFAR and MNIST datasets. The evaluation focused on model accuracy over 100 global iterations. Figure 7 shows the accuracy trends for the CIFAR dataset. MACS achieved the highest final accuracy, reaching approximately 95%, outperforming RL-based (92%), DL-based (91%), Static selection (85%), and Random selection (80%). The improved performance of MACS is due to its mobility prediction and resource-aware selection, which ensured stable participation from clients with sufficient resources. RL-based and DL-based approaches showed strong accuracy improvements but fluctuated in later iterations due to changes in network conditions. Static selection performed moderately but was limited by its fixed client assignments. Random selection exhibited the most variation, as it did not account for data quality or computational capacity. Figure 8 presents the accuracy trends for the MNIST dataset. MACS again achieved the highest final accuracy at 98%, followed by RL-based (96%), DL-based (95%), Static selection (92%), and Random selection (88%). The simpler nature of the MNIST dataset resulted in faster convergence for all methods. MACS maintained its advantage by consistently selecting reliable clients and avoiding unnecessary training delays. RL-based and DL-based methods performed well but were occasionally affected by mispredictions in client selection. Static selection achieved reasonable accuracy but lacked adaptability. Random selection remained the least effective due to inconsistent client participation. The results confirm that MACS improves accuracy and stability in federated learning by dynamically selecting clients based on mobility and resource availability, making it effective in dynamic IoT environments. MACS outperforms Random selection in latency and computational capacity by prioritizing clients with stable network connections and sufficient processing power. Unlike Random selection, which does not consider network quality, MACS selects  
Figure 7: Accuracy comparison for CIFAR dataset. clients with reliable bandwidth, reducing delays in model updates. Similarly, MACS ensures that chosen clients have adequate computational resources, preventing slowdowns caused by low-performance devices. While Random selection may occasionally perform comparably if it selects high-bandwidth or high-power clients, this outcome is inconsistent. MACS maintains stable performance across iterations, while Random selection leads to unpredictable training times and varying efficiency. MACS is designed to scale efficiently as the number of IoT clients increases and as model complexity grows. MACS prioritizes clients with stable connections and sufficient computational resources to handle large-scale networks. This approach prevents delays and ensures consistent global model updates. MACS dynamically adjusts selection thresholds for more complex models that require higher processing power, providing only capable clients participate and reducing dropout rates. While MACS introduces additional computation for mobility prediction and resource-aware selection, it remains lightweight compared to Deep Reinforcement Learning-based approaches. Its reliance on real-time network assessments instead of complex historical data processing makes it suitable for large-scale deployments. Future optimizations, such as hierarchical client selection through edge nodes, could enhance scalability by reducing selection overhead. MACS predict client availability by evaluating real-time network conditions and computational capacity, ensuring reliable participation in federated learning. It prioritizes clients with stable connections while filtering out those experiencing frequent signal drops or bandwidth fluctuations.