[2603.28006] FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning

Computer Science > Machine Learning arXiv:2603.28006 (cs) [Submitted on 30 Mar 2026] Title:FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning Authors:Brianna Mueller, W. Nick Street View a PDF of the paper titled FedDES: Graph-Based Dynamic Ensemble Selection for Personalized Federated Learning, by Brianna Mueller and W. Nick Street View PDF HTML (experimental) 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...