Protect What Matters, Hide Where: Selective Differential Privacy with Encrypted Masks for Federated Learning

Federated Learning (FL) enables collaborative model training without sharing raw data, yet exchanging model updates still poses privacy leakage risks. Existing privacy-preserving methods based on Homomorphic Encryption (HE) or Differential Privacy (DP) often entail substantial computational and communication overhead or result in degraded model utility, respectively. To address these challenges, we propose FedHPP, an efficient hybrid privacy-preserving FL framework that combines model sparsity-aware DP protection with HE-encrypted privacy mask techniques. Generally, FedHPP identifies a block of critical parameters using a Top-$K$ selection mechanism based on local model update magnitudes and applies DP noise exclusively to this block. Simultaneously, it uses HE to protect compact binary masks that pinpoint DP-injected and Non DP-injected parameter positions, concealing clients' private parameter selection patterns while substantially reducing encryption overhead. Aggregated masks guide clients in denoising, mitigating the impact of perturbations on model convergence. Extensive experiments conducted on real-world datasets under varying degrees of data heterogeneity and comparison against 10 baselines, demonstrating FedHPP's effectiveness in balancing three-way tensions among FL privacy, utility, and efficiency.