FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning

Brianna Mueller; W. Nick Street

doi:10.48550/arxiv.2603.28006

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FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning

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FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning

Brianna Mueller and W. Nick Street

ArXiv.org

Cornell University

03/30/2026

DOI: 10.48550/arxiv.2603.28006

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Statistical heterogeneity in Federated Learning (FL) often leads to negative transfer, where a single global model fails to serve diverse client distributions. Personalized federated learning (pFL) aims to address this by tailoring models to individual clients. However, under most existing pFL approaches, clients integrate peer client contributions uniformly, which ignores the reality that not all peers are likely to be equally beneficial. Additionally, the potential for personalization at the instance level remains largely unexplored, even though the reliability of different peer models often varies across individual samples within the same client. We introduce FedDES (Federated Dynamic Ensemble Selection), a decentralized pFL framework that achieves instance-level personalization through dynamic ensemble selection. Central to our approach is a Graph Neural Network (GNN) meta-learner trained on a heterogeneous graph modeling interactions between data samples and candidate classifiers. For each test query, the GNN dynamically selects and weights peer client models, forming an ensemble of the most competent classifiers while effectively suppressing contributions from those that are irrelevant or potentially harmful for performance. Experiments on CIFAR-10 and real-world ICU healthcare data demonstrate that FedDES outperforms state-of-the-art pFL baselines in non-IID settings, offering robust protection against negative transfer.

Computer Science - Learning

Details

Title: Subtitle: FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning
Creators: Brianna Mueller
W. Nick Street
Resource Type: Preprint
Publication Details: ArXiv.org
DOI: 10.48550/arxiv.2603.28006
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 03/30/2026
Academic Unit: Bus Admin College; Nursing; Computer Science; Business Analytics
Record Identifier: 9985149087302771

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