arXiv Statistics ML October 27, 2026 By Thibault Pautrel, Florent Bouchard, Guillaume Ginolhac, Ammar Mian, Aur\'elien Bellet

Projection-Based Federated Learning on Compact Submanifolds with Geometry-Independent Differential Privacy

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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.

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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

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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.

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arXiv AI

Jul 8

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arXiv Machine Learning

Aug 3

Differentially Private Nonparametric Modal Learning with Applications to Regression and Clustering

arXiv:2607. 29675v1 Announce Type: cross Abstract: Density modes provide a localized and interpretable summary of multimodal distributions, but their estimation under rigorous differential privacy constraints remains largely unexplored.

By Arkajyoti Bhattacharjee, Arnab Auddy

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arXiv AI

Sep 30

A Sharp Transition in Data Reconstruction under Differential Privacy

arXiv:2609.37344v1 Announce Type: cross Abstract: Data reconstruction attacks have empirically been successful in recovering training samples from learned models, raising privacy concerns and motivat...

By Max Cairney-Leeming, Simone Bombari, Marco Mondelli

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