Twice the Gradient, Twice the Privacy Risk in Federated Learning? A Case Study of Federated Recommendation Systems

Federated learning mitigates data leakage risks while maintaining training efficiency via gradient sharing. Nonetheless, previous studies have demonstrated persistent privacy vulnerabilities because attackers can reconstruct training data from shared gradients. Existing reconstruction methods assume that attackers can access all model parameters; however, sensitive parameters (such as user embeddings in federated recommendation systems) often remain private. Limited access results in inaccurate reconstructions. Using federated recommendation systems as a case study, we identify insufficient attack constraints as the origin of reconstruction failures. To address this limitation, we propose the MGradInv method, which leverages gradients from multiple training steps as additional reconstruction constraints. The experimental results demonstrate that this approach prevents convergence to local optima and reduces reconstruction errors by establishing sufficient constraints. We investigated two key factors affecting MGradInv's performance: target model convergence and gradient intervals. Results indicate that attacks are most effective during the early training stages but deteriorate as the model converges. The effects of MGradInv are clear even with gradient intervals of up to 230 steps. Our code and data are available here.