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

TL;DR

FedLECC introduces a cluster- and loss-guided client selection method for federated learning that enhances model accuracy and reduces communication costs under non-IID data conditions.

Contribution

It proposes a novel, lightweight client selection strategy that groups clients by label similarity and prioritizes high-loss clients to improve federated learning efficiency.

Findings

Up to 12% test accuracy improvement

Approximately 22% reduction in communication rounds

Up to 50% decrease in communication overhead

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.