Graph Federated Learning Based Proactive Content Caching in Edge Computing

TL;DR

This paper introduces a privacy-preserving, graph neural network-based federated learning approach for proactive content caching in edge computing, significantly improving cache efficiency and user privacy over traditional methods.

Contribution

It proposes a novel Graph Federated Learning framework that combines federated learning with graph neural networks for accurate, privacy-preserving content popularity prediction in edge caching.

Findings

Outperforms baseline caching algorithms in cache efficiency.

Enhances privacy by sharing only model parameters, not raw data.

Demonstrates effectiveness on real-world datasets like MovieLens.

Abstract

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.