  Recent studies have revealed severe privacy risks in federated learning,
represented by Gradient Leakage Attacks. However, existing studies mainly aim
at increasing the privacy attack success rate and overlook the high computation
costs for recovering private data, making the privacy attack impractical in
real applications. In this study, we examine privacy attacks from the
perspective of efficiency and propose a framework for improving the Efficiency
of Privacy Attacks in Federated Learning (EPAFL). We make three novel
contributions. First, we systematically evaluate the computational costs for
representative privacy attacks in federated learning, which exhibits a high
potential to optimize efficiency. Second, we propose three early-stopping
techniques to effectively reduce the computational costs of these privacy
attacks. Third, we perform experiments on benchmark datasets and show that our
proposed method can significantly reduce computational costs and maintain
comparable attack success rates for state-of-the-art privacy attacks in
federated learning. We provide the codes on GitHub at
https://github.com/mlsysx/EPAFL.
