# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
###### Abstract
While some approaches try to address this by being smart about resource usage or using reinforcement learning to adapt to changes, they often fall short in highly mobile settings. They might be unable to balance choosing diverse clients and ensuring stable participation [9; 10]. Existing strategies that consider mobility also have their limitations. For example, FedCS [11] tries to pick clients based on their computing and communication abilities. Still, it assumes clients will always be available, which is not true in high-mobility environments. Latency-Aware FL [9] focuses on picking clients with low latency, but it does not predict future movements, so clients often drop out unexpectedly. Vehicular FL models [12] work well for vehicles following set routes but do not handle unpredictable movement in unstructured IoT scenarios. Reinforcement learning-based methods [13] can dynamically adjust client selection but require a lot of computational power, which is not always feasible for small IoT devices. This motivated the development of Mobility-Aware Client Selection (MACS). MACS combines predictive modeling of how clients will move, real-time evaluation of their resources, and adaptive selection mechanisms. It does not just look at what is happening right now; it predicts future states to make smarter choices. MACS ensures stable and efficient participation in FL, even when devices are constantly moving. By doing this, MACS speeds up convergence and keeps the model more stable than previous methods. While MACS was initially designed with IoT networks in mind, its applications extend far beyond. It can be instrumental in any environment where client mobility plays a significant role, and stable selection is crucial for Federated Learning (FL). MACS can revolutionize traffic monitoring, environmental sensing, and public safety in smart cities by picking reliable, high-resource nodes. MACS ensures that models receive continuous updates, leading to better urban management. In healthcare, MACS, focusing on stable wearable devices, optimizes remote patient monitoring and medical AI models. MACS boosts diagnostic accuracy and minimizes disruptions in real-time health tracking. These examples show how versatile MACS is; it thrives in dynamic, data-driven environments where efficient and stable FL participation is key. This paper introduces Mobility-AwareClient Selection (MACS), a groundbreaking strategy that predicts client movement. MACS can pick clients who are likely to stay connected and perform consistently by doing this. This predictive power helps strike a balance between choosing mobile clients that bring diverse data and stable clients that ensure reliable participation. As a result, MACS makes FL more efficient, speeds up model convergence, and cuts down on latency. MACS predict and evaluates and checks each client's computational capabilities and network conditions before selecting them for training rounds. Moreover, MACS ensures that only the best candidates are chosen. Plus, it has a dynamic resource allocation framework that adjusts resources on the fly based on client feedback. This adaptability is especially valuable in IoT setups, where devices move around and have different capabilities. In short, MACS is a game-changer. It tackles the challenges of mobility and resource heterogeneity head-on, making FL more effective in dynamic IoT environments. The main contributions of this paper are as follows:  
1. A mobility prediction model that forecasts clients' future states, ensuring stable and efficient participation in FL rounds. 2. A resource-aware evaluation mechanism to select clients based on predicted computational and communication capacities, improving FL performance. 3. A dynamic resource allocation strategy to optimize real-time resource utilization, addressing variability in network conditions and client requirements. 4. A comprehensive evaluation of MACS through simulations, demonstrating its advantages in dynamic IoT environments, particularly in addressing mobility and resource constraints.