SelectiveShield: Lightweight Hybrid Defense Against Gradient Leakage in Federated Learning

TL;DR

Selectively combining homomorphic encryption and differential privacy based on parameter sensitivity, SelectiveShield provides a lightweight, adaptive defense against gradient leakage in federated learning, balancing privacy, utility, and system efficiency.

Contribution

Introduces SelectiveShield, a hybrid defense framework that adaptively protects critical parameters using encryption and privacy noise, improving security without sacrificing model performance.

Findings

Effective mitigation of gradient leakage attacks.

Maintains high model utility with reduced overhead.

Scalable and adaptable to heterogeneous FL environments.

Abstract

Federated Learning (FL) enables collaborative model training on decentralized data but remains vulnerable to gradient leakage attacks that can reconstruct sensitive user information. Existing defense mechanisms, such as differential privacy (DP) and homomorphic encryption (HE), often introduce a trade-off between privacy, model utility, and system overhead, a challenge that is exacerbated in heterogeneous environments with non-IID data and varying client capabilities. To address these limitations, we propose SelectiveShield, a lightweight hybrid defense framework that adaptively integrates selective homomorphic encryption and differential privacy. SelectiveShield leverages Fisher information to quantify parameter sensitivity, allowing clients to identify critical parameters locally. Through a collaborative negotiation protocol, clients agree on a shared set of the most sensitive parameters for protection via homomorphic encryption. Parameters that are uniquely important to individual clients are retained locally, fostering personalization, while non-critical parameters are protected with adaptive differential privacy noise. Extensive experiments demonstrate that SelectiveShield maintains strong model utility while significantly mitigating gradient leakage risks, offering a practical and scalable defense mechanism for real-world federated learning deployments.