通过聚类与损失引导选择客户端，提升非独立同分布下的联邦学习效率。
FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
- 按标签分布相似性聚类客户端，优先选择高损失群体。
- 在严重标签偏斜下准确率最高提升12%，通信轮次减少22%。
- 适合资源受限的跨设备联邦学习场景，提升训练效率。
联邦学习（FL）通过分布式协同训练，在不集中数据的前提下实现云边环境中的人工智能。在跨设备部署中，系统面临严格的通信与参与限制，以及强非独立同分布（non-IID）数据，导致收敛变慢、模型质量下降。由于每轮训练仅部分设备（客户端）可参与，智能客户端选择成为关键系统挑战。本文提出FedLECC（增强聚类选择的联邦学习），一种轻量级、聚类感知且损失引导的客户端选择策略。该方法根据标签分布相似性分组客户端，并优先选择局部损失较高的集群与客户端，从而选出少量但信息丰富且多样化的客户端集合。实验结果表明，在极端标签偏斜条件下，相比强基线方法，FedLECC可将测试准确率提升最高达12%，通信轮次减少约22%，总体通信开销降低最多50%。结果表明，有策略的客户端选择显著提升了云边系统中联邦学习工作的效率与可扩展性。
原文摘要 · Abstract (English)
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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