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

arXiv: https://arxiv.org/abs/2508.04100 · HTML: https://mlsanity.com/paper/2508.04100/

## 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.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [TiFL: A Tier-based Federated Learning System](https://mlsanity.com/paper/2001.09249/) (2020)
- [Faster gaze prediction with dense networks and Fisher pruning](https://mlsanity.com/paper/1801.05787/) (2018)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Global Sparse Momentum SGD for Pruning Very Deep Neural Networks](https://mlsanity.com/paper/1909.12778/) (2019)
- [A Gradient Flow Framework For Analyzing Network Pruning](https://mlsanity.com/paper/2009.11839/) (2020)
- [Aggregation Service for Federated Learning: An Efficient, Secure, and More Resilient Realization](https://mlsanity.com/paper/2202.01971/) (2022)
- [PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization](https://mlsanity.com/paper/2103.01548/) (2021)
- [FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.00675/) (2021)
- [No Parameters Left Behind: Sensitivity Guided Adaptive Learning Rate for Training Large Transformer Models](https://mlsanity.com/paper/2202.02664/) (2022)
- [Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization](https://mlsanity.com/paper/2212.08272/) (2022)
- [Dubhe: Towards Data Unbiasedness with Homomorphic Encryption in Federated Learning Client Selection](https://mlsanity.com/paper/2109.04253/) (2021)
- [Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost](https://mlsanity.com/paper/2112.04261/) (2021)
- [The Importance of Being Parameters: An Intra-Distillation Method for Serious Gains](https://mlsanity.com/paper/2205.11416/) (2022)
- [Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2309.07197/) (2023)

## 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.