# SecDD: Efficient and Secure Method for Remotely Training Neural Networks

arXiv: https://arxiv.org/abs/2009.09155 · HTML: https://mlsanity.com/paper/2009.09155/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2020.

## Built on by (most cited first, 14 of 14)

- [Dataset Distillation by Matching Training Trajectories](https://mlsanity.com/paper/2203.11932/) (2022)
- [Dataset Distillation: A Comprehensive Review](https://mlsanity.com/paper/2301.07014/) (2023)
- [Improved Distribution Matching for Dataset Condensation](https://mlsanity.com/paper/2307.09742/) (2023)
- [Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching](https://mlsanity.com/paper/2310.05773/) (2023)
- [Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environments](https://mlsanity.com/paper/2208.11311/) (2022)
- [Dataset Condensation via Generative Model](https://mlsanity.com/paper/2309.07698/) (2023)
- [Towards Robust Dataset Learning](https://mlsanity.com/paper/2211.10752/) (2022)
- [Improve Cross-Architecture Generalization on Dataset Distillation](https://mlsanity.com/paper/2402.13007/) (2024)
- [MGD3: Mode-Guided Dataset Distillation using Diffusion Models](https://mlsanity.com/paper/2505.18963/) (2025)
- [Label-Augmented Dataset Distillation](https://mlsanity.com/paper/2409.16239/) (2024)
- [ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillation](https://mlsanity.com/paper/2602.23295/) (2026)
- [EVLF: Early Vision-Language Fusion for Generative Dataset Distillation](https://mlsanity.com/paper/2603.07476/) (2026)
- [UDD: Dataset Distillation via Mining Underutilized Regions](https://mlsanity.com/paper/2408.16268/) (2024)
- [What Makes a Good Example? Modeling Exemplar Selection with Neural Network Representations](https://mlsanity.com/paper/2602.03144/) (2026)

## Abstract (arXiv)

We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecured channels.