# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 2 Related Work  
Federated Learning (FL) has been widely explored for training models across edge devices while keeping data private. However, dealing with mobile clients is still a big challenge. Traditional methods often assume network conditions stay the same, which is not always true in real-world scenarios. Take FedAvg [4], for example. It picks clients randomly or based on their computing power, assuming they will stay connected throughout the training process. However, in environments where devices are constantly moving or losing connection, this approach does not work. FedCS [11] tried to improve things by considering communication limits, but it did not factor in client mobility. So when devices move out of range, they drop out frequently, disrupting the training process. Similarly, FedRDS [14] used regularization and data-sharing techniques to handle client drift and boost model accuracy. While it did well in some aspects, it did not tackle the mobility issue, making it less effective in dynamic IoT settings. Latency-Aware FL [9] aimed to speed up training by choosing clients with fast connections. However, it struggled with unstable participation without predicting mobility as client availability kept changing. [PERSON] et al. [15] looked at FL in vehicular edge computing, developing ways to handle rapid client movement. These methods worked well in structured environments like vehicular networks, but they do not adapt as well to IoT applications, where device movement is often unpredictable. Existing methods have made strides in various areas, but they fall short when handling the unique challenges of dynamic IoT environments. This is where new approaches, like Mobility-Aware Client Selection (MACS), come into play. MACS addresses these issues head-on, ensuring stable and efficient FL even when clients are on the move. Some studies have employed reinforcement learning (RL) to optimize client selection dynamically. For example, [PERSON] et al. [13] applied RL in NOMA-enabled FL to improve client selection, adapting to changing network conditions. However, such methods require extensive computational resources, making them impractical for resource-constrained IoT devices. [PERSON] et al. [16] introduced a latency-aware selection framework to minimize communication delays. While effective in controlled settings, the lack of mobility prediction limits its adaptability in highly dynamic environments. Recent advancements in RL-based client selection have shown promise in addressing these challenges. [PERSON] et al. [17] proposed an RL-based approach using Deep Q-Learning (DQL) to optimize client selection in FL. Their method selects clients based on resource availability, data quality, and past contributions, balancing model convergence speed and fairness. Despite its effectiveness, this approach assumes relatively stable client availability and does not explicitly account for mobility-induced instability. [PERSON] et al. [18] addressed the challenge of non-IID data in FL by using Proximal Policy Optimization (PPO) to select clients dynamically. Their RL agent learns to prioritize clients whose data distributions contribute most effectively to global model convergence. However, this method focuses on data heterogeneity and does not consider mobility-induced instability. [PERSON] et al. [19] explored Multi-Agent Reinforcement Learning (MARL) for client selection in FL. Each client is modeled as an agent, and the RL framework optimizes the selection process by considering local and global objectives. While MARL enhances scalability, it introduces additional computational overhead, which may not be feasible for resource-constrained IoT devices. [PERSON] et al. [20] introduced an adaptive client selection strategy using Deep Reinforcement Learning (DRL). Their approach dynamically selects clients by considering factors like computational capabilities, network conditions, and data quality. Essentially, DRL learns over time to make better choices about which clients to include in each training round.