HADES: Privacy-Preserving Federated Learning via Selective Feature Encryption and Hybrid Model Fusion
2026-06-22 • Cryptography and Security
Cryptography and Security
AI summaryⓘ
The authors propose HADES, a method for training machine learning models using federated learning that protects privacy by encrypting only the most sensitive features instead of everything. They use PCA to find which data features need protection and apply special encryption only on those, while the rest stays unencrypted for faster processing. HADES combines results from both encrypted and unencrypted parts using a fusion step, maintaining accuracy similar to normal training but with better privacy and efficiency. They also optimize the encryption process to reduce computational overhead.
federated learningprivacy-preservingfeature encryptionPCA (principal component analysis)homomorphic encryptionmultiparty computationfusion mechanismreconstruction attackscomputational efficiency
Authors
Ergün Batuhan Kaynak, Kerem Bayramoglu, Sinem Sav
Abstract
In this paper, we address the challenge of privacy-preserving training in federated learning (FL) by introducing a novel framework that selectively encrypts only the most privacy-sensitive features while leaving the remaining data and the corresponding model portion unencrypted. We propose HADES, a hybrid system that identifies and encrypts the most critical features, ensuring both privacy protection and computational efficiency. Unlike fully encrypted FL training pipelines, which suffer from high computational overhead, HADES integrates an encrypted and non-encrypted training pipeline via a fusion mechanism, enabling seamless interaction between encrypted and plaintext model representations. To achieve this, we use PCA to identify and encrypt the most privacy-sensitive features, which significantly reduces reconstruction attack success in FL. Building on this insight, we design a hybrid FL system that trains an end-to-end encrypted network via multiparty homomorphic encryption (MHE) on the selected features while simultaneously training a plaintext network on the remaining features. These two networks are then integrated using a fusion mechanism. We also introduce a general packing scheme that eliminates redundant rotations by considering the entire neural network architecture. Finally, we demonstrate that HADES matches the accuracy of vanilla FL while preserving privacy and achieving optimized runtime through selective encryption.