Amplified Gradient Inversion Attacks on Federated Learning Frameworks

Federated Learning (FL) facilitates collaborative model training while safeguarding data privacy, making it ideal for sensitive fields such as finance, education, and healthcare. Despite its promise, FL remains vulnerable to privacy breaches, particularly through gradient inversion attacks that can reconstruct private data from shared model updates. This research introduces a nonlinear amplification strategy that enhances the potency of such attacks, revealing heightened risks of data leakage in FL environments. Additionally, we evaluate the resilience of privacy-preserving mechanisms, such as Differential Privacy (DP) and Homomorphic Encryption (HE), by employing two proposed metrics AvgSSIM and AvgMSE to measure both the severity of attacks and the efficacy of defenses.