We propose a novel approach to exploit such evidential asymmetry in FL aggregation in not independent and identically distributed (non-IID) data.

We propose a novel approach to exploit such evidential asymmetry in FL aggregation in not independent and identically distributed (non-IID) data.

Abstract. Federated Learning (FL) is a collaborative machine learning paradigm in which a global model is learned via aggregating local ones.

Jan 3, 2023 � In federated learning, client models are often trained on local training sets that vary in size and distribution. Such statistical�...

A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In�...

TL;DR: This work presents one-shot federated learning, where a central server learns a global model over a network of federated devices in a single round of�...

Jun 1, 2023 � Robust federated learning under statistical heterogeneity via Hessian spectral decomposition ; Journal. Pattern Recognition ; Volume. 141 ; Article�...

Robust federated learning under statistical heterogeneity via Hessian spectral decomposition � Author Picture Adnan Ahmad. School of Information Technology�...

Robust federated learning under statistical heterogeneity via Hessian spectral decomposition. 1 Sep 2023Pattern Recognition141:14 pagesELSEVIER SCI LTD. Co�...

Co-authors ; Robust federated learning under statistical heterogeneity via Hessian spectral decomposition. A Ahmad, W Luo, A Robles-Kelly. Pattern Recognition�...