FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data

Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis +1 more
3/9/2026
cs.DCcs.AIcs.LG

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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.

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Cite this paper

@article{jimenezgutierrez2026fedlecc,
  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},
  year   = {2026},
  eprint = {2603.08911},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2603.08911v1}
}

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