Intelligent Systems

QuanCrypt-FL is a communication-efficient federated learning algorithm that integrates low-bit quantization, pruning, and mean-based clipping to enhance privacy protection against inference attacks while minimizing computational costs and maintaining high model accuracy. Validated on MNIST, CIFAR-10, and CIFAR-100 datasets, QuanCrypt-FL outperforms state-of-the-art methods, achieving significantly faster encryption, decryption, and training times compared to BatchCrypt.

A hybrid privacy-preserving algorithm combining Pre-Aggregation Similarity Measurement (PA-SM) and Differential Privacy (DP) enhances the security and resilience of Federated Learning (FL) in Intelligent Transportation Systems (ITS) by defending against adversarial attacks while maintaining model performance.

A hybrid privacy-preserving algorithm combining Pre-Aggregation Similarity Measurement (PA-SM) and Differential Privacy (DP) enhances the security and resilience of Federated Learning (FL) in Intelligent Transportation Systems (ITS) by defending against adversarial attacks while maintaining model performance.

QuanCrypt-FL is a communication-efficient federated learning algorithm that integrates low-bit quantization, pruning, and mean-based clipping to enhance privacy protection against inference attacks while minimizing computational costs and maintaining high model accuracy. Validated on MNIST, CIFAR-10, and CIFAR-100 datasets, QuanCrypt-FL outperforms state-of-the-art methods, achieving significantly faster encryption, decryption, and training times compared to BatchCrypt.