Evaluating Gradient Leakage Attacks in Federated Learning for Distributed Computing Systems

Federated learning (FL) enables collaborative model training across distributed clients without sharing raw data, offering a promising solution for privacy-sensitive applications. However, recent studies have shown that FL is vulnerable to gradient leakage attacks (GLAs), where adversaries can reconstruct private training data from shared model gradients. In this paper, we conduct a comprehensive empirical study to evaluate the effectiveness of GLAs in federated learning systems. Specifically, we systematically analyze the impact of both data-related factors (e.g., data distribution, batch size) and model-related factors (e.g., activation function, network layer) on attack performance. Extensive experiments on benchmark datasets (MNIST and CIFAR-10) reveal that these factors significantly influence the success rate of GLAs. Based on our findings, we provide actionable insights and practical recommendations to mitigate privacy risks, contributing to the development of more dependable FL systems in real-world distributed computing environments.