A Secure Distributed Learning Framework Using Homomorphic Encryption

The increasing complexity of artificial intelligence (AI) models poses a significant challenge for individuals and organizations without sufficient computing resources to train them. While cloud-based training services can offer a solution, they require sharing sensitive data with untrusted parties, posing risks to data privacy. To address this challenge, we explore the combination of distributed training and homomorphic encryption to parallelize the training process on encrypted data. We utilize the CKKS homomorphic encryption scheme to develop a framework that can train comparably accurate AI models in less time than other homomorphically encrypted training solutions. Our experiments demonstrate reduced total runtime for homomor-phically encrypted model training while maintaining competitive classification accuracy for the MNIST handwritten digits dataset, a well-known benchmarking dataset for machine learning. Our framework brings homomorphic encryption closer to becoming a practical data privacy solution for small stakeholders who cannot afford to compromise on security.