# Graph Federated Learning Based Proactive Content Caching in Edge Computing

arXiv: https://arxiv.org/abs/2502.04760 · HTML: https://mlsanity.com/paper/2502.04760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 20 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [A Reinforcement-Learning Approach to Proactive Caching in Wireless Networks](https://mlsanity.com/paper/1712.07084/) (2017)
- [Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching](https://mlsanity.com/paper/2001.03835/) (2020)
- [Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks](https://mlsanity.com/paper/1606.04236/) (2016)
- [Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks](https://mlsanity.com/paper/1405.5974/) (2014)
- [A Non-Stationary Bandit-Learning Approach to Energy-Efficient Femto-Caching with Rateless-Coded Transmission](https://mlsanity.com/paper/2005.04154/) (2020)
- [Proactive Caching for Energy-Efficiency in Wireless Networks: A Markov Decision Process Approach](https://mlsanity.com/paper/1711.10710/) (2017)
- [Energy-Efficient Proactive Caching with Multipath Routing](https://mlsanity.com/paper/2104.13493/) (2021)

## Abstract (arXiv)

With the rapid growth of mobile data traffic and the increasing prevalence of video streaming, proactive content caching in edge computing has become crucial for reducing latency and alleviating network congestion. However, traditional caching strategies such as FIFO, LRU, and LFU fail to effectively predict future content popularity, while existing proactive caching approaches often require users to upload data to a central server, raising concerns regarding privacy and scalability. To address these challenges, this paper proposes a Graph Federated Learning-based Proactive Content Caching (GFPCC) scheme that enhances caching efficiency while preserving user privacy. The proposed approach integrates federated learning and graph neural networks, enabling users to locally train Light Graph Convolutional Networks (LightGCN) to capture user-item relationships and predict content popularity. Instead of sharing raw data, only the trained model parameters are transmitted to the central server, where a federated averaging algorithm aggregates updates, refines the global model, and selects the most popular files for proactive caching. Experimental evaluations on real-world datasets, such as MovieLens, demonstrate that GFPCC outperforms baseline caching algorithms by achieving higher cache efficiency through more accurate content popularity predictions. Moreover, the federated learning framework strengthens privacy protection while maintaining efficient model training; however, scalability remains a challenge in large-scale networks with dynamic user preferences.