Mar 24, 2021 · Abstract:Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL).
In this paper, we propose a correlation-based active client selection strategy that can effectively alleviate the accuracy degradation caused by data.
In this paper, we propose a correlation-based active client selection strategy that can effectively alleviate the accuracy degradation caused by data.
➢A uniform client selection strategy in FL might not be the optimal with heterogeneous clients. ➢Intuition. ➢Clients do not contribute equivalently. ➢Clients do ...
In this work, we propose FedCor---an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
In this work, we propose FedCoran FLframework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
This work proposes FedCoran FLframework built on a correlation-based client selection strategy, to boost the convergence rate of FL and develops an ...
Dec 3, 2022 · PDF | On Jun 1, 2022, Minxue Tang and others published FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous ...
Sep 11, 2024 · In this work, we propose FedGP -- a federated learning framework built on a correlation-based client selection strategy, to boost the ...
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This work proposes FedGP—a federated learning framework built on a correlation-based client selection strategy, to boost the convergence rate of FL.
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