Hugging Face Blog
Introducing the Private Hub: A New Way to Build With Machine Learning
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arXiv Machine Learning
Sep 14
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.
arXiv AI
Jun 18
Private Learning with Public Feature Conditioning
arXiv:2606. 18773v1 Announce Type: cross Abstract: We study differentially private (DP) regression in settings where each data sample includes public, non-sensitive features -- common in applications such as recommendation and advertising systems.
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.
arXiv Machine Learning
Sep 3
Private Computation Space: Experience with Trusted Multi-Cluster Federated Learning for Agriculture
The paper introduces the Private Computation Space (PCS), an open‑source federated learning system designed for agriculture that protects farmer data using asynchronous federated learning, differential privacy, and trusted execution environments. PCS runs on commodity hardware and is resilient to rural infrastructure challenges. In two real‑world deployments—nitrogen monitoring in New York and evapotranspiration prediction in California—PCS achieved a Dice Similarity Coefficient of 0.71 and an $R^2$ of 0.84, improving single‑site model accuracy by 22.4% and 9.1% respectively while preserving privacy.
arXiv AI
Aug 18
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.
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.