Graph Federated Learning Based Proactive Content Caching in Edge Computing
Rui Wang
TL;DR
This work tackles proactive content caching in edge computing under privacy and scalability constraints. It introduces GFPCC, a privacy-preserving framework that merges lightweight graph collaborative filtering with federated learning in a hierarchical edge-cloud setup to predict content popularity from local data and aggregate updates without sharing raw data. The approach leverages Light Graph Convolutional Network principles and a q-FedAvg aggregation scheme to provide fair and efficient global learning, demonstrating superior cache efficiency on MovieLens data compared to several baselines. The findings indicate strong practical potential for privacy-respecting, scalable proactive caching in dynamic mobile networks, while highlighting scalability as an open challenge for very large deployments.
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.