Exploring and Mitigating Gradient Leakage Vulnerabilities in Federated Learning
Harshit Kumar Gupta, Ghena Barakat, Luca D’Agati, Francesco Longo, Giovanni Merlino, Antonio Puliafito · 2025
Due to the importance of data privacy, Federated Learning (FL) is increasingly adopted in various applications. Its importance lies in sharing only local model gradients with the central server instead of the raw training data. The model gradients shared for aggregation are key components of FL. However, these gradients are mathematical values and are prone to attack, which may lead to data leakage issues. Therefore, implementing robust security measures is crucial to prevent data leakage when sharing model gradients with the central server. Such measures are essential to protect gradients from being exploited by attackers to retrieve real data. This work proposed and demonstrated how public gradients can be used to retrieve private training data and how this may be avoided by using the technique of differential privacy.