Enhanced Shuffle Models for Optimized Differential Privacy in Federated Learning

The core innovation is a refined shuffle model using a secure "invisible cloak" - based protocol. This eliminates the need for a trusted shuffler and simplifies data security without complex cryptography. IS - FL also features a new mechanism for random selection and noise injection during training. By selectively applying Laplacian noise to gradient data, it safeguards strong privacy while minimizing accuracy loss. Experiments on real - world datasets show that IS - FL outperforms traditional DP - FL, LDP - FL, and the state - of - the - art SS - FL. At the same privacy budget, IS - FL has notably higher test accuracy. For example, at a privacy budget of (2.0, 5 e - 6), it retains 99% of non - privacy - preserving FL accuracy, showing its excellent privacy - performance balance.