Zero-Knowledge AI Enhancing Data Privacy in Federated Learning Models

Abstract

With the increasing need to train AI models on sensitive healthcare data, Federated Learning (FL) has emerged as a decentralized approach that avoids raw data sharing. However, existing methods such as DP-FL and zkFL still suffer from high privacy leakage, computational overhead, and scalability challenges. To overcome these limitations, this study introduces ZK-FedTransformer++, a novel privacy-preserving FL framework. It integrates lightweight TinyViT transformers, zk-SNARKs for verifiable training, differential privacy for statistical protection, and heuristic client selection for robust participation The approach provides secure model updates via cryptographic proof circuits and noise-perturbed gradients. Experiments based on the RSNA Breast Cancer Detection dataset achieve 91.2% accuracy and 35% less privacy leakage. Tools utilized include PyTorch, zk-SNARK libraries, and privacy accounting protocols. In summary, ZK- FedTransformer++ is an effective privacy enhancement, accuracy improvement, and scalability solution that is a feasible solution for secure, decentralized AI applications in real-world healthcare and IoT settings.