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; Andrea Vitaletti

arXiv · 2603.08911

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·Andrea Vitaletti

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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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Daniel M. Jimenez-Gutierrez, Giovanni Giunta, Mehrdad Hassanzadeh, Aris Anagnostopoulos, Ioannis Chatzigiannakis, Andrea Vitaletti. 2026-03-09. FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data. https://arxiv.org/abs/2603.08911

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