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Found 4 papers, 1 papers with code
Online Sensitivity Optimization in Differentially Private Learning
Training differentially private machine learning models requires constraining an individual's contribution to the optimization process.
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond
FL was proposed as a stepping-stone towards privacy-preserving machine learning, but it has been shown vulnerable to issues such as leakage of private information, lack of personalization of the model, and the possibility of having a trained model that is fairer to some groups than to others.
Group privacy for personalized federated learning
To cope with the issue of protecting the privacy of the clients and allowing for personalized model training to enhance the fairness and utility of the system, we propose a method to provide group privacy guarantees exploiting some key properties of $d$-privacy which enables personalized models under the framework of FL.
Predictive Auto-scaling with OpenStack Monasca
Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e. g., the average CPU usage among instances, exceeds a predefined threshold.
