← Back to all news
arXiv Machine Learning

A Differentially Private Federated Proximal Optimization Framework for Customer Churn Prediction in Heterogeneous Federated Telecom Networks

The paper introduces a Differentially Private Federated Proximal (DP‑FedProx) framework for predicting customer churn in telecom networks. It compares DP‑FedProx with FedAvg, DP‑FedAvg, FedProx, and several centralized/local models on two public churn datasets, showing that DP‑FedProx consistently outperforms FedAvg variants and matches the best centralized model with only a slight accuracy loss while offering privacy guarantees. SHAP analysis reveals that DP‑FedProx prioritizes revenue‑group features, highlighting its practical balance between performance and privacy.

Related stories

arXiv Machine Learning
Aug 27

Theoretically Principled Federated Learning for Balancing Privacy and Utility

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
benchmarkssafety
More like this →
arXiv AI
4d ago

Privacy-enhanced federated learning via asynchronous aggregation and local differential perturbation

arXiv:2609.15885v1 Announce Type: cross Abstract: This study proposes a privacy-enhanced federated learning framework to address secure collaborative training in distributed data environments. The fr...

By Zhen Zhong (Georgetown University, Washington, D.C., USA), Shini Yang (LinkedIn, CA, USA), Liesheng Wei (Shanghai Ocean University, Shanghai, China)
benchmarkssafety
More like this →
arXiv Machine Learning
Sep 4

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task involving fraud and clinical‑risk scoring, where it improves the F1‑score for a minority class from 0.030 (plain FedAvg) to 0.119 while bounding privacy loss. The study demonstrates that privacy and robustness mechanisms interact, and that system design for regulated, adversarial, cross‑institutional settings must account for this interaction.

By Srikumar Nayak
safety
More like this →
Hugging Face Trending Papers
Sep 2

Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems

The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task for fraud and clinical‑risk scoring, showing that plain FedAvg fails when a quarter of twenty clients are Byzantine, while DP‑BR‑FedAvg recovers more signal and bounds privacy loss. The study demonstrates that privacy and robustness interact, and system design for regulated, adversarial, cross‑institutional settings must account for this interaction.

safety
More like this →
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
More like this →