4 papers
Gradient Projection onto Historical Descent Directions for Communication-Efficient Federated Learning
Arnaud Descours, Léonard Deroose, Jan Ramon
Federated Learning (FL) enables decentralized model training across multiple clients while optionally preserving data privacy. However, communication efficiency remains a critical…
Dropout-Robust Mechanisms for Differentially Private and Fully Decentralized Mean Estimation
César Sabater, Sonia Ben Mokhtar, Jan Ramon
Achieving differentially private computations in decentralized settings poses significant challenges, particularly regarding accuracy, communication cost, and robustness against in…
Differentially Private Empirical Cumulative Distribution Functions
Antoine Barczewski, Amal Mawass, Jan Ramon
In order to both learn and protect sensitive training data, there has been a growing interest in privacy preserving machine learning methods. Differential privacy has emerged as an…
DP-SGD with weight clipping
Antoine Barczewski, Jan Ramon
Recently, due to the popularity of deep neural networks and other methods whose training typically relies on the optimization of an objective function, and due to concerns for data…