Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network

• URL: http://arxiv.org/abs/2401.09886v2
• Date: Wed, 5 Jun 2024 00:35:48 GMT
• Title: Cooperative Edge Caching Based on Elastic Federated and Multi-Agent Deep Reinforcement Learning in Next-Generation Network
• Authors: Qiong Wu, Wenhua Wang, Pingyi Fan, Qiang Fan, Huiling Zhu, Khaled B. Letaief,
• Abstract summary: Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs) It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. We propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network.
• Score: 24.731109535151568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Edge caching is a promising solution for next-generation networks by empowering caching units in small-cell base stations (SBSs), which allows user equipments (UEs) to fetch users' requested contents that have been pre-cached in SBSs. It is crucial for SBSs to predict accurate popular contents through learning while protecting users' personal information. Traditional federated learning (FL) can protect users' privacy but the data discrepancies among UEs can lead to a degradation in model quality. Therefore, it is necessary to train personalized local models for each UE to predict popular contents accurately. In addition, the cached contents can be shared among adjacent SBSs in next-generation networks, thus caching predicted popular contents in different SBSs may affect the cost to fetch contents. Hence, it is critical to determine where the popular contents are cached cooperatively. To address these issues, we propose a cooperative edge caching scheme based on elastic federated and multi-agent deep reinforcement learning (CEFMR) to optimize the cost in the network. We first propose an elastic FL algorithm to train the personalized model for each UE, where adversarial autoencoder (AAE) model is adopted for training to improve the prediction accuracy, then {a popular} content prediction algorithm is proposed to predict the popular contents for each SBS based on the trained AAE model. Finally, we propose a multi-agent deep reinforcement learning (MADRL) based algorithm to decide where the predicted popular contents are collaboratively cached among SBSs. Our experimental results demonstrate the superiority of our proposed scheme to existing baseline caching schemes.

Related papers

• Resource-Aware Hierarchical Federated Learning in Wireless Video Caching Networks [24.664469755746463]
  Backhaul traffic congestion caused by the video traffic of a few popular files can be alleviated by storing the to-be-requested content. We propose a novel resource-aware hierarchical federated learning (RawHFL) solution for predicting user's future content requests.
  arXiv  Detail & Related papers  (2024-02-06T18:17:02Z)
• CLSA: Contrastive Learning-based Survival Analysis for Popularity Prediction in MEC Networks [36.01752474571776]
  Mobile Edge Caching (MEC) integrated with Deep Neural Networks (DNNs) is an innovative technology with significant potential for the future generation of wireless networks. The MEC network's effectiveness heavily relies on its capacity to predict and dynamically update the storage of caching nodes with the most popular contents. To be effective, a DNN-based popularity prediction model needs to have the ability to understand the historical request patterns of content.
  arXiv  Detail & Related papers  (2023-03-21T15:57:46Z)
• Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks [34.44384973176474]
  Coded/uncoded content placement in Mobile Edge Caching (MEC) has evolved to meet the significant growth of global mobile data traffic. Most existing datadriven popularity prediction models are not suitable for the coded/uncoded content placement frameworks. We develop a Multiple-model (hybrid) Transformer-based Edge Caching (MTEC) framework with higher generalization ability.
  arXiv  Detail & Related papers  (2022-10-12T02:24:49Z)
• Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning [28.564236336847138]
  vehicular edge computing (VEC) can cache contents in different RSUs at the network edge to support the real-time vehicular applications. Traditional federated learning (FL) needs to update the global model synchronously through aggregating all users' local models to protect users' privacy. We propose a cooperative Caching scheme in the VEC based on Asynchronous Federated and deep Reinforcement learning (CAFR)
  arXiv  Detail & Related papers  (2022-08-02T03:09:08Z)
• Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach [66.31587753595291]
  We propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. For mobile users, the content popularity is predicted via user preference learning.
  arXiv  Detail & Related papers  (2022-06-13T03:34:00Z)
• An Expectation-Maximization Perspective on Federated Learning [75.67515842938299]
  Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated federated learning process as a hierarchical latent variable model where the server provides the parameters of a prior distribution over the client-specific model parameters. We show that with simple Gaussian priors and a hard version of the well known Expectation-Maximization (EM) algorithm, learning in such a model corresponds to FedAvg, the most popular algorithm for the federated learning setting.
  arXiv  Detail & Related papers  (2021-11-19T12:58:59Z)
• Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching [91.50631418179331]
  A privacy-preserving distributed deep policy gradient (P2D3PG) is proposed to maximize the cache hit rates of devices in the MEC networks. We convert the distributed optimizations into model-free Markov decision process problems and then introduce a privacy-preserving federated learning method for popularity prediction.
  arXiv  Detail & Related papers  (2021-10-20T02:48:27Z)
• Learning from Images: Proactive Caching with Parallel Convolutional Neural Networks [94.85780721466816]
  A novel framework for proactive caching is proposed in this paper. It combines model-based optimization with data-driven techniques by transforming an optimization problem into a grayscale image. Numerical results show that the proposed scheme can reduce 71.6% computation time with only 0.8% additional performance cost.
  arXiv  Detail & Related papers  (2021-08-15T21:32:47Z)
• Caching Placement and Resource Allocation for Cache-Enabling UAV NOMA Networks [87.6031308969681]
  This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA) We formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP) We propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with emphsoft $varepsilon$-greedy strategy to search for the optimal match between actions and states.
  arXiv  Detail & Related papers  (2020-08-12T08:33:51Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)
• Reinforcement Learning Based Cooperative Coded Caching under Dynamic Popularities in Ultra-Dense Networks [38.44125997148742]
  caching strategy at small base stations (SBSs) is critical to meet massive high data rate requests. We exploit reinforcement learning (RL) to design a cooperative caching strategy with maximum-distance separable (MDS) coding.
  arXiv  Detail & Related papers  (2020-03-08T10:45:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.