Search Results for author:
Found 4 papers, 3 papers with code
Push--Pull with Device Sampling
We consider decentralized optimization problems in which a number of agents collaborate to minimize the average of their local functions by exchanging over an underlying communication graph.
Federated Learning with Superquantile Aggregation for Heterogeneous Data
We present a federated learning framework that is designed to robustly deliver good predictive performance across individual clients with heterogeneous data.
First-order Optimization for Superquantile-based Supervised Learning
Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution.
Device Heterogeneity in Federated Learning: A Superquantile Approach
We propose a federated learning framework to handle heterogeneous client devices which do not conform to the population data distribution.
