mlsanityData to 14 Oct 2026

SenseCrypt: Sensitivity-guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios

arXiv 2508.04100 ↗First posted 6 Aug 2025cs.CR cs.AI cs.DCcited by 0

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Abstract (arXiv)

Homomorphic Encryption (HE) prevails in securing Federated Learning (FL), but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in cross-device scenarios with heterogeneous data and system capabilities, traditional Selective HE methods deteriorate client straggling, and suffer from degraded HE overhead reduction performance. Accordingly, we propose SenseCrypt, a Sensitivity-guided selective Homomorphic EnCryption framework, to adaptively balance security and HE overhead per cross-device FL client. Given the observation that model parameter sensitivity is effective for measuring clients' data distribution similarity, we first design a privacy-preserving method to respectively cluster the clients with similar data distributions. Then, we develop a scoring mechanism to deduce the straggler-free ratio of model parameters that can be encrypted by each client per cluster. Finally, for each client, we formulate and solve a multi-objective model parameter selection optimization problem, which minimizes HE overhead while maximizing model security without causing straggling. Experiments demonstrate that SenseCrypt ensures security against the state-of-the-art inversion attacks, while achieving normal model accuracy as on IID data, and reducing training time by 58.4%-88.7% as compared to traditional HE methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 6 Aug 2025.

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21 of 42 references.

References · 21

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9 more
  1. 2022Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realizationcited by 19
  2. 2021PFA: Privacy-preserving Federated Adaptation for Effective Model Personalizationcited by 18
  3. 2021FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learningcited by 15
  4. 2022No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Modelscited by 15
  5. 2022Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantizationcited by 14
  6. 2021Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selectioncited by 7
  7. 2021Efficient Batch Homomorphic Encryption for Vertically Federated XGBoostcited by 6
  8. 2022The Importance of Being Parameters: An Intra-Distillation Method for Serious Gainscited by 6
  9. 2023Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environmentscited by 2

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