FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866911500800622592 |
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| author | Jimenez-Gutierrez, Daniel M. Giunta, Giovanni Hassanzadeh, Mehrdad Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea |
| author_facet | Jimenez-Gutierrez, Daniel M. Giunta, Giovanni Hassanzadeh, Mehrdad Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_08911 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data Jimenez-Gutierrez, Daniel M. Giunta, Giovanni Hassanzadeh, Mehrdad Anagnostopoulos, Aris Chatzigiannakis, Ioannis Vitaletti, Andrea Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning 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. |
| title | FedLECC: Cluster- and Loss-Guided Client Selection for Federated Learning under Non-IID Data |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.08911 |