## 7 Conclusions  
This paper proposed and evaluated an FDRL system for dynamic radio and MEC resource allocation and slicing management in O-RAN environments. By addressing the limitations of centralized DRL systems, the FDRL framework demonstrated its capability to enhance scalability, data privacy, and adaptability across diverse high-mobility scenarios, such as smart cities.  
The key contributions of this research are as follows:  
1. **Improved scalability and adaptability**: The federated approach allowed multiple agents to collaboratively train a shared RL model without centralizing interaction data. This not only ensured robust performance in varied traffic conditions but also enabled parallel learning across heterogeneous environments.
2. **Enhanced privacy and security**: By decentralizing the learning process, the system preserved user data privacy, which is critical in sensitive applications involving edge devices.
3. **Superior performance over centralized models**: The proposed framework achieved faster convergence compared to centralized DRL models and reduced bandwidth consumption by 58%. Additionally, the system effectively handled dynamic resource allocation and complex slice management scenarios, maintaining high-quality service under varying network conditions.
4. **Practical application for next-generation O-RAN deployments**: The system was tested under high-mobility conditions, demonstrating stable performance in smart city scenarios and event-based mobility environments. These results underline the feasibility of deploying FDRL-based slicing management in real-world O-RAN infrastructures.  
Although the proposed system presents significant improvements, certain challenges remain. The efficiency of MEC resource allocation is dependent on initial configuration parameters, and federated learning introduces additional processing overhead due to aggregation rounds and security mechanisms such as TLS encryption. Future work will explore optimization techniques to mitigate these challenges, including lightweight DRL models and adaptive learning strategies.  
The findings from this study highlight the potential of FL frameworks in transforming resource management for next-generation cellular networks. Future research can further extend this approach by incorporating hybrid learning models and optimizing energy efficiency in federated deployments.  
Conceptualization, M.M.-M., C.R.d.M., C.C.-P., and S.S.-R.; methodology, M.M.-M. and C.C.-P.; software, M.M.-M. and C.R.d.M.; validation, C.R.d.M., C.C.-P., and S.S.-R.; formal analysis, M.M.-M. and C.C.-P.; investigation, M.M.-M.; resources, C.C.-P. and S.S.-R.; data curation, M.M.-M. and C.R.d.M.; writing--original draft preparation, M.M.-M.; writing--review and editing, M.M.-M., C.R.d.M., C.C.-P., and S.S.-R.; visualization, M.M.-M. and C.C.-P.; supervision, C.C.-P. and S.S.-R.; project administration, C.C.-P. and S.S.-R.; funding acquisition, C.C.-P. and S.S.-R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Spanish Ministry of Economic Affairs and Digital Transformation; the European Union--NextGenerationEU in the framework of the Recovery Plan, Transformation, and Resilience (PRTR) (Call UNICO I+D 5G 2021, ref. number TSI-063000-2021-3/6/7--OPEN6G); and by the Agencia Estatal de Investigacion of Ministerio de Ciencia e Innovacion of Spain under project PID2022-137329 OB-C41/MCIN/AEI/10.13039/50110001103.  
**Data Availability Statement:** The software presented in this study is publicly available in a GitHub repository located at [[https://github.com/mmmorfa/FL_Model1](https://github.com/mmmorfa/FL_Model1)]([https://github.com/mmmorfa/FL_Model1](https://github.com/mmmorfa/FL_Model1)) (accessed on 10 January 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.