The paper introduces a general learning framework that protects privacy in federated learning by distorting model parameters, enabling a trade‑off between privacy and utility. The algorithm supports arbitrary privacy measurements and delivers personalized utility‑privacy balances for each parameter, client, and communication round. The authors prove that the gap between their algorithm’s utility loss and the optimal loss is sub‑linear in iterations, provide a convergence rate, and demonstrate empirically that their method outperforms baselines under the same privacy budget.
By Xiaojin Zhang, Wenjie Li, Yiming Li, Wei Chen, Shutao Xia, Qiang Yang
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
arXiv:2607. 23029v1 Announce Type: cross Abstract: Federated learning enables collaborative model training across distributed clients without centralising their data, yet privacy remains a persistent concern because the shared model updates can leak information about local datasets.
By Kun Zhao, Xu Chen
arXiv:2411.16478v3 Announce Type: replace
Abstract: Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analyti...
By Sayan Biswas, Graham Cormode, Carsten Maple, Mary Scott
arXiv:2609.14778v1 Announce Type: new
Abstract: We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model. Samples are lear...
By Erkan Bayram, Mohamed-Ali Belabbas, Tamer Ba\c{s}ar
The paper proposes a privacy‑aligned personalized federated learning method that releases a private client context once and limits repeated adaptation to a fixed coefficient space, thereby reducing dimensionality misalignment. A factorized generator creates an adaptive optimization geometry that reshapes noisy updates, and most of the private‑training benefit is preserved by radial evolution. Variable‑length Gaussian quantization is used for coefficient updates, allowing the quantization error to act as the privacy perturbation and cutting protected uplink communication by a factor of 2.67 on CIFAR‑10 at ε=16 while maintaining comparable future‑client accuracy.
By Yilin Xu, Chun Hei Michael Shiu, Chih Wei Ling, Linqi Song
Record-level differential privacy exposes a structural misalignment in personalized federated learning when client-specific variation is low-dimensional while training repeatedly releases high-dimensi...
arXiv:2608.28198v1 Announce Type: new
Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in...
By Uddalak Mukherjee, Edwige Cyffers, Yann Chevaleyre
arXiv:2505. 22703v2 Announce Type: replace Abstract: Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds, etc.
By Mohammad Yaghini, Tudor Cebere, Michael Menart, Aur\'elien Bellet, Nicolas Papernot
arXiv:2605. 05905v2 Announce Type: replace Abstract: Objective perturbation is a standard mechanism in differentially private empirical risk minimization.
By Daniel Cortild, Coralia Cartis
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
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