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When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Network

arXiv 2009.10601 ↗First posted 22 Sep 2020cs.NI cs.AI cs.DCcited by 29

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Abstract (arXiv)

Ultra-dense edge computing (UDEC) has great potential, especially in the 5G era, but it still faces challenges in its current solutions, such as the lack of: i) efficient utilization of multiple 5G resources (e.g., computation, communication, storage and service resources); ii) low overhead offloading decision making and resource allocation strategies; and iii) privacy and security protection schemes. Thus, we first propose an intelligent ultra-dense edge computing (I-UDEC) framework, which integrates blockchain and Artificial Intelligence (AI) into 5G ultra-dense edge computing networks. First, we show the architecture of the framework. Then, in order to achieve real-time and low overhead computation offloading decisions and resource allocation strategies, we design a novel two-timescale deep reinforcement learning (textit2Ts-DRL) approach, consisting of a fast-timescale and a slow-timescale learning process, respectively. The primary objective is to minimize the total offloading delay and network resource usage by jointly optimizing computation offloading, resource allocation and service caching placement. We also leverage federated learning (FL) to train the textit2Ts-DRL model in a distributed manner, aiming to protect the edge devices' data privacy. Simulation results corroborate the effectiveness of both the textit2Ts-DRL and FL in the I-UDEC framework and prove that our proposed algorithm can reduce task execution time up to 31.87%.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2020.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 7; 2024: 7; 2025: 4; 2026: 1.

29 papers.

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  1. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  2. 2022Data Heterogeneity-Robust Federated Learning via Group Client Selection in Industrial IoTcited by 25
  3. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  4. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  5. 2023Cooperative Multi-Agent Reinforcement Learning: Asynchronous Communication and Linear Function Approximationcited by 9
  6. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  7. 2022New Challenges in Reinforcement Learning: A Survey of Security and Privacycited by 7
  8. 2023FedHQL: Federated Heterogeneous Q-Learningcited by 6
  9. 2021Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challengescited by 5
  10. 2025FOVA: Offline Federated Reinforcement Learning with Mixed-Quality Datacited by 5
  11. 2022ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Samplingcited by 4
  12. 2021Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Surveycited by 3
17 more
  1. 2023Collaborative Policy Learning for Dynamic Scheduling Tasks in Cloud-Edge-Terminal IoT Networks Using Federated Reinforcement Learningcited by 3
  2. 2023Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applicationscited by 3
  3. 2024Compressing Deep Reinforcement Learning Networks with a Dynamic Structured Pruning Method for Autonomous Drivingcited by 3
  4. 2025Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networkscited by 3
  5. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computingcited by 2
  6. 2024Federated Prompt-based Decision Transformer for Customized VR Services in Mobile Edge Computing Systemcited by 2
  7. 2025FedADP: Unified Model Aggregation for Federated Learning with Heterogeneous Model Architecturescited by 2
  8. 2022Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approachcited by 1
  9. 2023Privacy-Preserving Joint Edge Association and Power Optimization for the Internet of Vehicles via Federated Multi-Agent Reinforcement Learningcited by 1
  10. 2023User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networkscited by 1
  11. 2024PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learningcited by 1
  12. 2024Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiencycited by 1
  13. 2024Federated Offline Policy Optimization with Dual Regularizationcited by 1
  14. 2025FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systemscited by 1
  15. 2024Privacy-Aware Spectrum Pricing and Power Control Optimization for LEO Satellite Internet-of-Thingscited by 0
  16. 2024Single-Loop Federated Actor-Critic across Heterogeneous Environmentscited by 0
  17. 2026FedQHD: Closed-Form Function-Space Federated Reinforcement Learningcited by 0