Search Results for author:
Found 3 papers, 0 papers with code
Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies
The rapid rise of IoT and Big Data has facilitated copious data driven applications to enhance our quality of life.
Information-Theoretic Bounds on the Generalization Error and Privacy Leakage in Federated Learning
Second is the distributed setting in which each device trains its own model and send its model parameters to a central server where these model parameters are aggregated to create one final model.
The Capacity Achieving Distribution for the Amplitude Constrained Additive Gaussian Channel: An Upper Bound on the Number of Mass Points
Finally, the third part provides bounds on the number of points for the case of $n=1$ with an additional power constraint.
Information Theory Information Theory
Cannot find the paper you are looking for? You can
Submit a new open access paper.
