FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
Abstract
Federated Learning (FL) enables distributed Artificial Intelligence (AI) across cloud-edge environments by allowing collaborative model training without centralizing data. In cross-device deployments, FL systems face strict communication and participation constraints, as well as strong non-independent and identically distributed (non-IID) data that degrades convergence and model quality. Since only a subset of devices (a.k.a clients) can participate per training round, intelligent client selection becomes a key systems challenge. This paper proposes FedLECC (Federated Learning with Enhanced Cluster Choice), a lightweight, cluster-aware, and loss-guided client selection strategy for cross-device FL. FedLECC groups clients by label-distribution similarity and prioritizes clusters and clients with higher local loss, enabling the selection of a small yet informative and diverse set of clients. Experimental results under severe label skew show that FedLECC improves test accuracy by up to 12%, while reducing communication rounds by approximately 22% and overall communication overhead by up to 50% compared to strong baselines. These results demonstrate that informed client selection improves the efficiency and scalability of FL workloads in cloud-edge systems.
Cite
@article{arxiv.2603.08911,
title = {FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data},
author = {Daniel M. Jimenez-Gutierrez and Giovanni Giunta and Mehrdad Hassanzadeh and Aris Anagnostopoulos and Ioannis Chatzigiannakis and Andrea Vitaletti},
journal= {arXiv preprint arXiv:2603.08911},
year = {2026}
}
Comments
Accepted to the IEEE International Workshop on Intelligent Cloud Computing and Networking (ICCN) from the IEEE International Conference on Computer Communications (INFOCOM) 2026