Personalised Recommendation With Federated Lightweight Graph Convolutional Networks

Federated recommendation systems aim to provide personalised services in decentralised environments while preserving user privacy. However, under strict privacy constraints and limited local information, existing federated models struggle to capture user‐item interactions effectively. This paper proposes FedLGCN, a novel federated recommendation framework that combines Lightweight Graph Convolutional Networks with a privacy‐enhanced local training protocol. A hash‐based privacy‐aware graph augmentation strategy is introduced to enrich each client's local subgraph without disclosing sensitive neighbour information. Additionally, Local Differential Privacy is employed to perturb gradients before aggregation, providing robust protection against inference attacks. The Lightweight Graph Convolutional Networks‐based embedding module enables efficient and scalable representation learning with reduced communication and computational costs. Extensive experiments on three public benchmark datasets demonstrate that FedLGCN achieves a well‐balanced trade‐off among communication cost, recommendation accuracy and privacy protection, consistently outperforming several baseline methods in federated recommendation.