Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2603.08911 (cs)
[Submitted on 9 Mar 2026]
Title:FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
Authors: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.
| Comments: | |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2603.08911 [cs.DC] |
| (or arXiv:2603.08911v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2603.08911
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From: Daniel Mauricio Jimenez Gutierrez [view email][v1] Mon, 9 Mar 2026 20:28:17 UTC (434 KB)
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