# Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing

arXiv: https://arxiv.org/abs/2208.01736 · HTML: https://mlsanity.com/paper/2208.01736/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2022.

## Built on by (most cited first, 14 of 14)

- [Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities](https://mlsanity.com/paper/2405.10825/) (2024)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights](https://mlsanity.com/paper/2312.04688/) (2023)
- [Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning](https://mlsanity.com/paper/2509.14343/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [ML-based handover prediction over a real O-RAN deployment using RAN Intelligent controller](https://mlsanity.com/paper/2404.19671/) (2024)
- [Intelligent Attacks and Defense Methods in Federated Learning-enabled Energy-Efficient Wireless Networks](https://mlsanity.com/paper/2504.18519/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)
- [Physically Constrained Federated Additive Models for O-RAN SLA-Risk Prediction](https://mlsanity.com/paper/2607.21665/) (2026)
- [FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs](https://mlsanity.com/paper/2608.03852/) (2026)

## Abstract (arXiv)

Recently, open radio access network (O-RAN) has become a promising technology to provide an open environment for network vendors and operators. Coordinating the x-applications (xAPPs) is critical to increase flexibility and guarantee high overall network performance in O-RAN. Meanwhile, federated reinforcement learning has been proposed as a promising technique to enhance the collaboration among distributed reinforcement learning agents and improve learning efficiency. In this paper, we propose a federated deep reinforcement learning algorithm to coordinate multiple independent xAPPs in O-RAN for network slicing. We design two xAPPs, namely a power control xAPP and a slice-based resource allocation xAPP, and we use a federated learning model to coordinate two xAPP agents to enhance learning efficiency and improve network performance. Compared with conventional deep reinforcement learning, our proposed algorithm can achieve 11% higher throughput for enhanced mobile broadband (eMBB) slices and 33% lower delay for ultra-reliable low-latency communication (URLLC) slices.