# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
###### Abstract  
This paper presents the Mobility-Aware Client Selection (MACS) strategy, developed to address the challenges associated with client mobility in Federated Learning (FL). FL enables decentralized machine learning by allowing collaborative model training without sharing raw data, preserving privacy. However, client mobility and limited resources in IoT environments pose significant challenges to the efficiency and reliability of FL. MACS is designed to maximize client participation while ensuring timely updates under computational and communication constraints. The proposed approach incorporates a Mobility Prediction Model to forecast client connectivity and resource availability and a Resource-Aware Client Evaluation mechanism to assess eligibility based on predicted latencies. MACS optimizes client selection, improves convergence rates, and enhances overall system performance by employing these predictive capabilities and a dynamic resource allocation strategy. The evaluation includes comparisons with advanced baselines such as Reinforcement Learning-based FL (RL-based) and Deep Learning-based FL (DL-based), in addition to Static and Random selection methods. For the CIFAR dataset, MACS achieved a final accuracy of 95%, outperforming Static selection (85%), Random selection (80%), RL-based FL (90%), and DL-based FL (93%). Similarly, for the MNIST dataset, MACS reached 98% accuracy, surpassing Static selection (92%), Random selection (88%), RL-based FL (94%), and DL-based FL (96%). Additionally, MACS consistently required fewer iterations to achieve target accuracy levels, demonstrating its efficiency in dynamic IoT environments. This strategy provides a scalable and adaptable solution for sustainable federated learning across diverse IoT applications, including smart cities, healthcare, and industrial automation. federated learning; client selection; mobility prediction; dynamic IoT environments +
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where Federated Learning (FL) comes into play, offering a promising solution by allowing data to remain on the edge devices while still enabling collaborative model training. This approach reduces communication costs and keeps data more secure [4; 5]. While Federated Learning (FL) seems like an ideal solution, it does come with its own set of challenges. One major hurdle is client mobility. As devices move around, their network connections can become unstable, leading to inconsistent participation in the training process. Furthermore, the varying capabilities of these devices, such as differences in computational power, energy availability, and bandwidth, can result in unbalanced contributions. Some devices may struggle to keep up with the demands of FL, causing delays and reducing overall efficiency. These challenges are particularly pronounced in dynamic IoT environments, where devices frequently change locations and conditions. Traditional FL methods often assume more stable and homogeneous settings, which do not always align with the realities of IoT networks. Therefore, addressing client mobility and heterogeneity is crucial for effective FL implementation in such environments. Client mobility can mess things up for Federated Learning (FL). When devices are always on the move, it leads to unstable connections, inconsistent computing power, and clients that come and go unpredictably. All these factors cause delays, incomplete model updates, and slow down the whole process. Traditional methods like Federated Averaging (FedAvg) [6] assume that clients stay put and do not change much, which does not work in IoT networks where everything is constantly moving. Then, there is the issue of device differences. Some devices have more computing power, some have more energy, and others have better internet connections. This creates an uneven playing field where some devices struggle to keep up with FL requirements [7; 8].