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

arXiv 2603.08911 ↗First posted 9 Mar 2026cs.DC cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 25 references that are in the map. First posted on arXiv 9 Mar 2026.

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References · 12

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2018Federated Learning with Non-IID Datacited by 1,099
  6. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  7. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  8. 2021Towards Personalized Federated Learningcited by 462
  9. 2014RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Responsecited by 262
  10. 2021FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learningcited by 43
  11. 2022Application of federated learning techniques for arrhythmia classification using 12-lead ECG signalscited by 9
  12. 2025A Thorough Assessment of the Non-IID Data Impact in Federated Learningcited by 5

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