## 2 Related Work
The solution proposed in this paper overcomes these challenges by integrating FL with DRL, allowing for collaborative learning among multiple O-RAN instances while preserving privacy and improving adaptability to dynamic environments. FL has emerged as a promising paradigm to address these challenges by enabling decentralized training of ML models while preserving data privacy. Recent studies emphasize the integration of FL in the O-RAN to enhance network intelligence and support advanced use cases. For instance, the paper [8] reviewed FL's potential to improve data security and privacy in Next-Generation Wireless Networks (NextGs). The study discusses FL's cooperative learning approach, which enables secure model training without sharing raw data, making it particularly suited for disaggregated architectures like the O-RAN. To improve the efficiency of FL in resource-constrained environments, the authors in [9] introduced O-RANFed, a framework for client selection and resource allocation in FL tasks. By combining slicing-based client selection with deadline-aware resource optimization, this study demonstrated improvements in convergence speed and learning efficiency compared to traditional FL methods like FedAvg and FedProx. However, this approach does not fully integrate DRL-based decision-making mechanisms into the federated training process, which limits its ability to dynamically adapt to varying network conditions in real time. Another notable approach is the integration of FL with DRL for enhanced network performance. [PERSON] et al. [10] introduced an FL-based coordination mechanism for multiple xApps, optimizing power control and slice-based resource allocation in the O-RAN. Their study demonstrated a 25% improvement in throughput for eMBB slices and a 30% reduction in latency for URLLCs slices when compared to traditional centralized RL methods. However, their approach is primarily focused on power allocation and does not comprehensively address the joint optimization of MEC and RAN resources within O-RAN environments. In contrast, the proposed framework integrates MEC-aware slicing decisions along with FL-enhanced DRL, ensuring end-to-end resource optimization in heterogeneous O-RAN networks. Similarly, the study in [11] investigated the integration of FL and DRL for joint task offloading and fronthaul segment routing. Their work proposes a reward-based optimization framework to minimize the Cost of Delay (CoD) in task execution. Their resultsindicate that FL-assisted DRL reduces the average delay by approximately 40% compared to nonfederated RL models while ensuring task offloading efficiency. However, this study is tailored toward task scheduling and routing rather than dynamic slice admission and resource allocation in the O-RAN. The approach presented in our work extends FL-assisted DRL to slice admission control, PRB allocation, and MEC resource distribution, optimizing the QoS experience for diverse traffic profiles. Despite these promising contributions, existing works have either focused on FL at a high level without integrating DRL for adaptive decision making or applied DRL without leveraging the collaborative learning benefits of FL. This work addresses these gaps by unifying FL, DRL, the O-RAN, and MEC into a single scalable framework, allowing multiple distributed DRL agents to collaborate via federated learning, thereby improving generalization, adaptability, and privacy preservation in heterogeneous RAN environments. A comparative summary of these approaches is provided in Table 1, which highlights key performance metrics and the distinct advantages of the proposed framework. By leveraging FL's decentralized and privacy-preserving capabilities in combination with DRL's real-time adaptability, this study presents a unified and comprehensive approach that advances the state of the art in intelligent O-RAN slicing and resource allocation.