3 papers
cs.LG2025
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…
cs.CR2025
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…
cs.CR2025
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…