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Found 6 papers, 2 papers with code
Improved Stability and Generalization Guarantees of the Decentralized SGD Algorithm
On the contrary, we show, for convex, strongly convex and non-convex functions, that D-SGD can always recover generalization bounds analogous to those of classical SGD, suggesting that the choice of graph does not matter.
One-Shot Federated Conformal Prediction
In this paper, we introduce a conformal prediction method to construct prediction sets in a oneshot federated learning setting.
Refined Convergence and Topology Learning for Decentralized SGD with Heterogeneous Data
One of the key challenges in decentralized and federated learning is to design algorithms that efficiently deal with highly heterogeneous data distributions across agents.
Robust Kernel Density Estimation with Median-of-Means principle
In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE).
Learning the piece-wise constant graph structure of a varying Ising model
This work focuses on the estimation of multiple change-points in a time-varying Ising model that evolves piece-wise constantly.
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks
In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks.
