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A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks

arXiv 2206.11556 ↗First posted 23 Jun 2022eess.SPcited by 0

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Abstract (arXiv)

In this paper, cooperative edge caching problem is studied in fog radio access networks (F-RANs). Given the non-deterministic polynomial hard (NP-hard) property of the problem, a dueling deep Q network (Dueling DQN) based caching update algorithm is proposed to make an optimal caching decision by learning the dynamic network environment. In order to protect user data privacy and solve the problem of slow convergence of the single deep reinforcement learning (DRL) model training, we propose a federated reinforcement learning method with quantization (FRLQ) to implement cooperative training of models from multiple fog access points (F-APs) in F-RANs. To address the excessive consumption of communications resources caused by model transmission, we prune and quantize the shared DRL models to reduce the number of model transfer parameters. The communications interval is increased and the communications rounds are reduced by periodical model global aggregation. We analyze the global convergence and computational complexity of our policy. Simulation results verify that our policy has better performance in reducing user request delay and improving cache hit rate compared to benchmark schemes. The proposed policy is also shown to have faster training speed and higher communications efficiency with minimal loss of model accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 37 references that are in the map. First posted on arXiv 23 Jun 2022.

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References · 10

  1. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  2. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 419
  5. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  6. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 263
  7. 2013Predicting Parameters in Deep Learningcited by 224
  8. 2020When Deep Reinforcement Learning Meets Federated Learning: Intelligent Multi-Timescale Resource Management for Multi-access Edge Computing in 5G Ultra Dense Networkcited by 30
  9. 2017A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challengescited by 7
  10. 2019Distributed Edge Caching via Reinforcement Learning in Fog Radio Access Networkscited by 3

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