mlsanityData to 7 Oct 2026

SecDD: Efficient and Secure Method for Remotely Training Neural Networks

arXiv 2009.09155 ↗First posted 19 Sep 2020cs.LG stat.MLcited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Sep 2020.

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Citing papers per year: 2022: 3; 2023: 4; 2024: 3; 2025: 1; 2026: 3.

14 papers.

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  1. 2022Dataset Distillation by Matching Training Trajectoriescited by 437
  2. 2023Dataset Distillation: A Comprehensive Reviewcited by 126
  3. 2023Improved Distribution Matching for Dataset Condensationcited by 124
  4. 2023Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matchingcited by 118
  5. 2022Federated Learning via Decentralized Dataset Distillation in Resource-Constrained Edge Environmentscited by 49
  6. 2023Dataset Condensation via Generative Modelcited by 15
  7. 2022Towards Robust Dataset Learningcited by 9
  8. 2024Improve Cross-Architecture Generalization on Dataset Distillationcited by 7
  9. 2025MGD3: Mode-Guided Dataset Distillation using Diffusion Modelscited by 6
  10. 2024Label-Augmented Dataset Distillationcited by 5
  11. 2026EVLF: Early Vision-Language Fusion for Generative Dataset Distillationcited by 3
  12. 2026ManifoldGD: Training-Free Hierarchical Manifold Guidance for Diffusion-Based Dataset Distillationcited by 2
2 more
  1. 2024UDD: Dataset Distillation via Mining Underutilized Regionscited by 0
  2. 2026What Makes a Good Example? Modeling Exemplar Selection with Neural Network Representationscited by 0