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

arXiv: https://arxiv.org/abs/2401.09886 · HTML: https://mlsanity.com/paper/2401.09886/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 18 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement Learning](https://mlsanity.com/paper/2208.01219/) (2022)
- [High stable and accurate vehicle selection scheme based on federated edge learning in vehicular networks](https://mlsanity.com/paper/2208.01890/) (2022)
- [Towards V2I Age-aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method](https://mlsanity.com/paper/2208.01283/) (2022)
- [Delay-sensitive Task Offloading in Vehicular Fog Computing-Assisted Platoons](https://mlsanity.com/paper/2309.10234/) (2023)
- [URLLC-Awared Resource Allocation for Heterogeneous Vehicular Edge Computing](https://mlsanity.com/paper/2311.18352/) (2023)
- [AoI-Aware Resource Allocation for Platoon-Based C-V2X Networks via Multi-Agent Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2105.04196/) (2021)
- [Decentralized Power Allocation for MIMO-NOMA Vehicular Edge Computing Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2107.14772/) (2021)
- [Wireless Caching: Technical Misconceptions and Business Barriers](https://mlsanity.com/paper/1602.00173/) (2016)
- [Time-Dependent Performance Modeling for Platooning Communications at Intersection](https://mlsanity.com/paper/2208.01213/) (2022)
- [Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks](https://mlsanity.com/paper/1405.5974/) (2014)
- [Asynchronous Federated Learning Based Mobility-aware Caching in Vehicular Edge Computing](https://mlsanity.com/paper/2208.01236/) (2022)
- [Pricing and Resource Allocation via Game Theory for a Small-Cell Video Caching System](https://mlsanity.com/paper/1602.06063/) (2016)
- [Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks](https://mlsanity.com/paper/2005.07941/) (2020)
- [User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks](https://mlsanity.com/paper/2005.07942/) (2020)

## Built on by (most cited first, 18 of 18)

- [Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communications](https://mlsanity.com/paper/2407.06518/) (2024)
- [Distributed Deep Reinforcement Learning Based Gradient Quantization for Federated Learning Enabled Vehicle Edge Computing](https://mlsanity.com/paper/2407.08462/) (2024)
- [Deep-Reinforcement-Learning-Based AoI-Aware Resource Allocation for RIS-Aided IoV Networks](https://mlsanity.com/paper/2406.11245/) (2024)
- [Reconfigurable Intelligent Surface Assisted VEC Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2406.11318/) (2024)
- [Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08458/) (2024)
- [Mobility-Aware Federated Self-supervised Learning in Vehicular Network](https://mlsanity.com/paper/2408.00256/) (2024)
- [DRL-Based Resource Allocation for Motion Blur Resistant Federated Self-Supervised Learning in IoV](https://mlsanity.com/paper/2408.09194/) (2024)
- [DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV](https://mlsanity.com/paper/2411.13104/) (2024)
- [Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation](https://mlsanity.com/paper/2407.13123/) (2024)
- [Digital Twin Vehicular Edge Computing Network: Task Offloading and Resource Allocation](https://mlsanity.com/paper/2407.11310/) (2024)
- [A Survey on Semantic Communications in Internet of Vehicles](https://mlsanity.com/paper/2503.03767/) (2025)
- [Optimizing Age of Information in Vehicular Edge Computing with Federated Graph Neural Network Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2407.02342/) (2024)
- [A Comprehensive Survey on Joint Resource Allocation Strategies in Federated Edge Learning](https://mlsanity.com/paper/2410.07881/) (2024)
- [Federated Learning Assisted Edge Caching Scheme Based on Lightweight Architecture DDPM](https://mlsanity.com/paper/2506.04593/) (2025)
- [Resource Allocation for Twin Maintenance and Computing Task Processing in Digital Twin Vehicular Edge Computing Network](https://mlsanity.com/paper/2407.07575/) (2024)
- [Enhanced SPS Velocity-adaptive Scheme: Access Fairness in 5G NR V2I Networks](https://mlsanity.com/paper/2501.08037/) (2025)
- [TDC-Cache: A Trustworthy Decentralized Cooperative Caching Framework for Web3.0](https://mlsanity.com/paper/2512.09961/) (2025)
- [MACTS-EM: Multi-Agent Collaborative Time Series Forecasting with Emergent Memory](https://mlsanity.com/paper/2610.02255/) (2026)

## Abstract (arXiv)

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.