## 2 Related Work  
The development of a next-generation RAN, especially within the O-RAN framework, is marked by a strong focus on intelligence and openness. A key goal of these efforts is to create RANs that are self-configuring and self-optimizing, thereby being capable of adapting to complex and diverse environments that integrate various platforms, technologies, and vendor solutions. Research has extensively explored critical challenges such as RAN resource allocation and fragmentation in the context of the O-RAN and MEC, striving to optimize performance while maintaining adherence to stringent QoS requirements. However, existing approaches often focus on individual aspects of network intelligence--such as O-RAN-based ML optimization, MEC-aware resource management, or FL for decentralized learning--without offering a unified framework that holistically integrates these elements. For instance, [PERSON] et al. [1] employed actor-critic learning to optimize the resource allocation of Network Functions (NFs) between Centralized Units (CUs) and Distributed Units (DUs) in the O-RAN. This approach has been shown to lead to significant improvements in latency, throughput, and packet delivery ratios across a variety of traffic types and priorities. Similarly, the authors in [2] explored service slicing and baseband resource allocation tailored to distinct 5G service categories: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLCs), and Massive Machine-Type Communications (mMTCs). While these works offer valuable insights into optimizing O-RAN performance, they remain constrained to localized or single-layer optimizations without fully integrating MEC-based resource management or decentralized learning strategies. Furthermore, an xApp-based approach presented in [3] utilizes ML algorithms for dynamic resource allocation. By overcoming the limitations of static resource allocation, this method adjusts Physical Resource Blocks (PRBs) in real time to optimize network performance and reduce costs. Likewise, Reinforcement Learning (RL)-based controllers such as those in [4] adapt modulation and coding schemes based on traffic flow and channel quality, enhancing throughput. However, these approaches rely on centralized decision making or independent RL agents, which limits their ability to generalize across heterogeneous O-RAN deployments, particularly in dynamic environments with fluctuating traffic and network conditions. Despite these advancements, many studies focus solely on the O-RAN layer without fully integrating MEC, resulting in a gap in the management of both radio and edge computing resources in a unified manner. To address this gap, ref. [5] formulated a stochastic optimization problem for task offloading and resource allocation in MEC-enabled Cloud RAN environments. This semi-distributed framework balances energy efficiency and service delay, offering a balanced perspective on network performance trade-offs. Additionally, ref. [6] highlighted the role of Deep Learning (DL) in MEC resource allocation, covering task offloading, scheduling, and joint resource management. While these studies contribute valuable insights into MEC optimizations, they lack the integration of federated learning, a critical component for enhancing scalability and privacy in multi-client O-RAN deployments. Building on these foundations, ref. [7] proposed a comprehensive but centralized solution that bridges radio-level resource allocation and MEC management using two DRL models based on DQN. The first model optimizes PRB and time slot allocation, while the second ensures QoS compliance across network slices. While effective in centralized environments, this approach suffers from a lack of federated collaboration, relying solely on the individual experiences of each system instance for training. Consequently, it is prone to suboptimal learning when encountering unexpected network conditions and remains vulnerable to data privacy risks and scalability limitations.