###### Abstract  
The evolution of cellular networks from fifth-generation (5G) architectures to beyond 5G (B5G) and sixth-generation (6G) systems necessitates innovative solutions to overcome the limitations of traditional Radio Access Network (RAN) infrastructures. Existing monolithic and proprietary RAN components restrict adaptability, interoperability, and optimal resource utilization, posing challenges in meeting the stringent requirements of next-generation applications. The Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms, enabling disaggregation, virtualization, and real-time adaptability--which are key to achieving ultra-low latency, enhanced bandwidth efficiency, and intelligent resource management in future cellular systems. This paper presents a Federated Deep Reinforcement Learning (FDRL) framework for dynamic radio and edge computing resource allocation and slicing management in O-RAN environments. An Integer Linear Programming (ILP) model has also been developed, resulting in the proposed FDRL solution drastically reducing the system response time. On the other hand, unlike centralized Reinforcement Learning (RL) approaches, the proposed FDRL solution leverages Federated Learning (FL) to optimize performance while preserving data privacy and reducing communication overhead. Comparative evaluations against centralized models demonstrate that the federated approach improves learning efficiency and reduces bandwidth consumption. The system has been rigorously tested across multiple scenarios, including multi-client O-RAN environments and loss-of-synchronization conditions, confirming its resilience in distributed deployments. Additionally, a case study simulating realistic traffic profiles validates the proposed framework's ability to dynamically manage radio and computational resources, ensuring efficient and adaptive O-RAN slicing for diverse and high-mobility scenarios. F +
Footnote †: journal: Future Internet  
_Article_  
To overcome these challenges, the Open Radio Access Network (O-RAN) and Multi-Access Edge Computing (MEC) have emerged as transformative paradigms. The O-RAN promotes disaggregation and virtualization, enabling modular, interoperable, software-based RAN components, while MEC moves compute and application hosting to the network edge to support latency-sensitive services. This integration enables the utilization of the entire O-RAN ecosystem, encompassing its defined functions and open interfaces, to establish more direct and efficient automated control over RAN operations. This integration also ensures the availability of resources to meet demanding requirements. Although these advances offer promising solutions, the heterogeneity of B5G environments uncovers new challenges related to scalability, data security, and the diversity of Machine Learning (ML) models needed to automate RAN control. Federated learning (FL) has emerged as a revolutionary approach to tackling these challenges. Unlike traditional centralized ML systems, FL enables distributed model training by retaining data on local devices and only sharing model updates. This ensures data privacy while maintaining scalability, making it particularly suited for the O-RAN and MEC environments where sensitive, localized data are generated at the network edge. By integrating FL into the O-RAN, the flexibility and scalability needed to dynamically allocate resources, manage slicing admission control, and maintain Quality of Service (QoS) can be achieved across diverse and high-mobility deployment scenarios. This article proposes the implementation of multiple Deep Reinforcement Learning (DRL) systems with the objective of fostering federative collaboration across these systems to generate a unified global model. This model is designed to facilitate effective responses to heterogeneous environments, such as high-mobility scenarios.