HETAL: New Privacy-Preserving Method for Transfer Learning with Homomorphic Encryption

HETAL is an efficient homomorphic encryption-based algorithm for privacy-preserving transfer learning. It enables clients to encrypt data features and send them to a server for fine-tuning without compromising data privacy. HETAL's novel matrix multiplication algorithms and softmax approximation technique have potential applications in other areas involving neural networks and encrypted computations.