# FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments Source: [FedHENet: A Frugal Federated Learning Framework for Heterogeneous Environments]() Collection index: [llms.txt]() Data updated: 2026-02-17T05:00:04.915Z Authors: Alejandro Dopico-Castro, Oscar Fontenla-Romero, Bertha Guijarro-Berdi\\~nas, Amparo Alonso-Betanzos, Iv\\'an P\\'erez Dig\\'on Published: 2026-02-13T15:30:18.000Z ## Abstract Federated Learning (FL) enables collaborative training without centralizing data, essential for privacy compliance in real-world scenarios involving sensitive visual information. Most FL approaches rely on expensive, iterative deep network optimization, which still risks privacy via shared gradients. In this work, we propose FedHENet, extending the FedHEONN framework to image classification. By using a fixed, pre-trained feature extractor and learning only a single output layer, we avoid costly local fine-tuning. This layer is learned by analytically aggregating client knowledge in a single round of communication using homomorphic encryption (HE). Experiments show that FedHENet achieves competitive accuracy compared to iterative FL baselines while demonstrating superior stability performance and up to 70\\% better energy efficiency. Crucially, our method is hyperparameter-free, removing the carbon footprint associated with hyperparameter tuning in standard FL. Code available in https://github.com/AlejandroDopico2/FedHENet/ ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()