# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 2 Related Work
However, this method comes with a catch. It requires a lot of processing power, which can be a big issue in IoT environments where devices often have limited resources. While it is great at making smart decisions based on various factors, the high computational demands make it less practical for widespread use in IoT settings. Recent studies have also looked into hybrid approaches that combine Reinforcement Learning (RL) with other techniques to tackle specific challenges in Federated Learning (FL). For example, [PERSON] et al. [21] devised an energy-efficient way to pick clients using RL. Their method selects clients based on how much energy they use, the quality of their data, and communication costs. This approach reduces energy use while keeping the model accurate, making it a good fit for mobile and edge computing. [PERSON] et al. [22] took a different angle by introducing a fairness-aware RL mechanism. Their RL agent learns to balance getting the best model accuracy and ensuring all clients are fairly represented. While this addresses the fairness issue in client selection, it still struggles in environments where devices move around unpredictably. Despite these advancements, many existing RL-based methods are demanding regarding computational power, making them impractical for IoT devices that often have limited resources. Additionally, many of these methods do not explicitly account for the instability caused by device mobility, which is crucial in dynamic IoT settings. For instance, FedCS [11] and Latency-Aware FL [9] focus on communication quality and latency but do not predict mobility, leading to frequent dropouts when devices move out of range. Similarly, RL-based methods like those proposed by [PERSON] et al. [23] and [PERSON] et al. [24] introduce significant computational overhead, which is not ideal for IoT deployments with limited processing power. This work proposes that the Mobility-Aware Client Selection (MACS) framework tackles these issues by integrating mobility prediction, resource-aware client evaluation, and dynamic selection mechanisms. Unlike FedCS, which assumes clients stay available throughout training, MACS predicts device movement to ensure stable participation. Compared to Latency-Aware FL, MACS does not just rely on real-time latency conditions; it uses future mobility estimation to minimize dropouts. While effective for structured movement, methods designed for vehicles do not work as well for general IoT applications. MACS is built to handle both structured and unstructured mobility patterns. Moreover, MACS has a lower computational overhead than reinforcement learning-based approaches, which makes it more suitable for IoT devices with limited processing power. Ensuring the trustworthiness of data in FL is another challenge, as unreliable clients can introduce biased updates or security risks. [PERSON] et al. [25] suggested a feedback-based trust mechanism where clients assign trust scores based on observed data consistency. [PERSON] et al. [26] used a machine learning model to detect anomalies in IoT data exchanges, enhancing reliability. Nevertheless, these methods require continuous monitoring and additional computational resources, which may not always be feasible in FL due to communication constraints. MACS inherently improves data reliability by selecting clients with stable mobility patterns and sufficient computational resources, reducing the likelihood of unreliable contributions. Several studies have explored various client selection strategies in FL, but many fall short regarding high-mobility IoT environments. Table 1 provides a clear comparison of existing approaches. FedCS [11; 27; 28] picks clients based on communication quality but does not consider mobility, leading to unstable participation. Latency-Aware FL [9; 29] focuses on low-latency clients but lacks mobility prediction, causing frequent dropouts when network conditions change.