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.

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