arXiv Machine Learning June 26, 2026 By Naoki Nishikawa, Shokichi Takakura, Satoshi Hasegawa

Escaping Iterative Parameter-Space Noise: Differentially Private Learning with a Hypernetwork

Read the original on arXiv Machine Learning →

arXiv:2606. 26772v1 Announce Type: new Abstract: Differentially private (DP) training of neural networks is often hindered by the large amount of noise required by gradient-based methods such as DP-SGD, which repeatedly inject high-dimensional noise in parameter space throughout training.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 23

Differentially Private Neural Network Training Under the Hidden State Assumption

arXiv:2407. 08233v3 Announce Type: replace Abstract: Current differentially private learning paradigms face a severe utility bottleneck: DP-SGD degrades performance through noise accumulation over training steps, while aggregation-based approaches such as PATE suffer from data inefficiency due to disjoint data partitioning.

By Ding Chen, Haochen Luo, Xiaofei Wang, Chen Liu

benchmarks safety

arXiv Machine Learning

Jul 23

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

arXiv:2607. 19580v1 Announce Type: new Abstract: Differentially private machine learning enables model training on sensitive data while ensuring that individual data is unlikely to be recoverable from the parameters of the resulting model.

By Huaiyuan Rao, Calvin Hawkins, Alexander Benvenuti, Matthew Hale

benchmarks safety

arXiv Machine Learning

Jun 4

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data

arXiv:2606. 04399v1 Announce Type: new Abstract: In the paradigm of decentralized learning, a group of agents collaborate to train a global model using distributed datasets without a central server.

By Yunsheng Yuan, Xue Xiao, Lina Wang, Feng Li

agents benchmarks safety

arXiv AI

Jul 9

An Adaptive Differentially Private Federated Learning Framework

arXiv:2602. 06838v3 Announce Type: replace Abstract: Federated learning enables collaborative model training across distributed clients while preserving data privacy.

By Jin Wang, Hui Ma, Yajun Zhang, Xinjun Pei, Ming Yan, Fei Xing, Yikun Chen

safety

arXiv Machine Learning

Jul 28

DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton

arXiv:2607. 23649v1 Announce Type: new Abstract: Differential privacy provides formal privacy guarantees for training neural networks on sensitive data, while Bayesian deep learning offers a principled framework for uncertainty-aware prediction.

By Nour Jamoussi, Ikram Dridi, Giuseppe Serra, Marios Kountouris

safety

arXiv Machine Learning

Jun 16

Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy

arXiv:2407. 04884v4 Announce Type: replace Abstract: The hidden state threat model of differential privacy (DP) assumes that the adversary has access only to the final trained machine learning (ML) model, without seeing intermediate states during training.

By Rob Romijnders, Antti Koskela

benchmarks safety

Related stories

Differentially Private Neural Network Training Under the Hidden State Assumption

End-to-End Differential Privacy in Training Deep Neural Network Classifiers

DPDL: Towards Differential Privacy Preservation in Decentralized Stochastic Learning on Non-IID Data

An Adaptive Differentially Private Federated Learning Framework

DP-IVON-Gradsq: Differentially Private Squared-Gradient Improved Variational Online Newton

Convex Approximation of Two-Layer ReLU Networks for Hidden State Differential Privacy