## 1 Introduction
### Contributions and Novelties  
In this study, we present an in-depth exploration of Federated Learning (FL) methodology within the context of 6G technology, delving into its intricacies and challenges across heterogeneous nodes. Our work uniquely integrates hardware and software components, utilizing a distinctive combination of Raspberry Pi devices and virtual machines as FL clients, each equipped with diverse datasets sourced from the CIFAR10 dataset--a widely acknowledged benchmark in image classification. Our contributions and innovations can be outlined as follows:  
* **Cooling Mechanism Impact (Section 4.1):** We meticulously investigate the influence of cooling mechanisms on training accuracy, underscoring their practical significance in accelerating model convergence, especially in resource-constrained environments. This detailed analysis, expounded on in Section 4.1, elucidates the pivotal role of cooling mechanisms, providing valuable insights into optimizing FL performance.
* **Heterogeneous Client Compensation (Section 4.2):** Through a thorough exploration of asymmetric data distribution scenarios, both with and without random selection, we dissect the intricate dynamics of FL performance. Our study highlights the delicate balance necessary in distributing training data among diverse nodes, revealing the complexities of FL dynamics in real-world scenarios. These findings, presented in Section 4.2, offer critical insights into the challenges and solutions concerning data heterogeneity in FL setups.
* **Overfitting Mitigation Strategies (Section 4.2):** We tackle the challenge of overfitting in FL by implementing meticulous strategies. By integrating random selection techniques, we effectively mitigate overfitting risks, optimizing model generalization and ensuring the resilience of FL outcomes. This contribution, outlined in Section 4.2, underscores our commitment to enhancing the robustness of FL models.
* **Scalability Analysis (Section 4.3):** Our study provides a comprehensive exploration of FL scalability, assessing its performance with an increasing number of users. This analysis, detailed in Section 4.3, offers crucial insights into FL's scalability potential, essential for its integration in large-scale, dynamic environments. It emphasizes the system's adaptability to diverse user configurations, laying the foundation for FL's applicability in real-world scenarios.
* **Pretraining Effectiveness (Section 4.4):** We delve into the effectiveness of pretraining techniques in enhancing accuracy rates. Pretraining emerges as a potent tool, significantly boosting the model's performance and showcasing its potential in optimizing FL outcomes. This contribution, discussed in Section 4.4, highlights the practical implications of pretraining in FL applications, providing actionable insights for future implementations.
* **Transfer Learning Impact (Section 4.5):** In Section 4.5, we investigate the potential of Transfer Learning, evaluating its impact under diverse client configurations. Our results,underscore Transfer Learning's capacity to enhance FL model performance, especially in the face of varied client scenarios. This analysis showcases Transfer Learning's adaptability in real-world applications, emphasizing its role in improving FL outcomes across dynamic and heterogeneous environments.  
These contributions collectively form a comprehensive and innovative exploration of FL dynamics, addressing key challenges and offering practical solutions essential for the advancement of Federated Learning technologies in complex, real-world settings.