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.
Jan 3, 2023 · In federated learning, client models are often trained on local training sets that vary in size and distribution. Such statistical ...
Jun 5, 2024 · Robust federated learning under statistical heterogeneity via Hessian spectral decomposition ; Volume. 141 ; Article number. ARTN 109635 ...
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 ...
Jan 3, 2023 · Federated learning (FL) involves collaboration between clients with limited data to produce a single optimal global model through consensus. One ...
Nov 5, 2023 · Federated learning (FL) involves collaboration between clients with limited data to produce a single optimal global model through consensus.
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What is statistical heterogeneity in federated learning?
What is heterogeneous federated learning?
Robust federated learning under statistical heterogeneity via Hessian spectral decomposition. 1 Sep 2023Pattern Recognition141:14 pagesELSEVIER SCI LTD. Co ...
Robles-Kelly, “Robust federated learning under statistical heterogeneity via hessian spectral decomposition,” Pattern Recognition, vol. 141,p. 109 635, 2023 ...
Co-authors ; Robust federated learning under statistical heterogeneity via Hessian spectral decomposition. A Ahmad, W Luo, A Robles-Kelly. Pattern Recognition ...