arXiv AI August 18, 2026 By Fardin Jalil Piran, Zhiling Chen, Yang Zhang, Qianyu Zhou, Jiong Tang, Farhad Imani

Privacy-Preserving Decentralized Federated Learning via Explainable Adaptive Differential Privacy

arXiv:2509. 10691v3 Announce Type: replace-cross Abstract: Decentralized federated learning enables collaborative model training without a central server, but shared model updates can still leak sensitive information through inversion, reconstruction, and membership inference attacks.

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

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

Jun 9

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

arXiv:2604. 07125v2 Announce Type: replace-cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation.

By Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine

safety

arXiv Machine Learning

1d ago

An Adaptive Gradient Clipping and Noise Injection Mechanism for Differentially Private Federated Learning

arXiv:2608. 15153v1 Announce Type: cross Abstract: Differentially private federated learning must balance privacy protection against model accuracy and training efficiency.

By Wenjing Wei, Alla Jammine, Farid Nait-Abdesselam

safety

Hugging Face Trending Papers

Jun 1

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets ($\varepsilon_i$) according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ $\varepsilon$-aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets.

safety

arXiv Machine Learning

Jun 15

Private Prediction via PAC Privacy

arXiv:2601. 14033v2 Announce Type: replace Abstract: Machine learning models are increasingly served behind APIs.

By Xiaochen Zhu, Mayuri Sridhar, Srinivas Devadas

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