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FedCSGA: Client Selection Based on Genetic Algorithm for Federated Learning — arXiv:2611.04667

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04667
License (as archived): CC0-1.0
Archived: 2026-11-04T21:45:50.243Z
SHA-256 of the archived text: f3b1d6587bb3e2c95a1388e44ad244ecaaebce6890f6922214fd33927cd56099
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How to cite

FedCSGA: Client Selection Based on Genetic Algorithm for Federated Learning — arXiv:2611.04667 — arXiv (en), https://arxiv.org/abs/2611.04667 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e4173bd970d2 (Metraipolis archive dc_e4173bd970d2, sha256 f3b1d6587bb3e2c95a1388e44ad244ecaaebce6890f6922214fd33927cd56099)

BibTeX

@misc{dce4173bd970d2,
  title = {FedCSGA: Client Selection Based on Genetic Algorithm for Federated Learning — arXiv:2611.04667},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04667},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e4173bd970d2; sha256 f3b1d6587bb3e2c95a1388e44ad244ecaaebce6890f6922214fd33927cd56099},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04667. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Federated Learning (FL) enables collaborative training of machine learning models across distributed clients while preserving data privacy. In practical deployments, FL relies on selecting a subset of clients in each communication round to reduce communication cost, mitigate stragglers, and improve training efficiency under heterogeneous data and system conditions. In heterogeneous edge computing environments, both statistical heterogeneity and system heterogeneity significantly affect convergence efficiency and model performance. However, existing client selection methods often address these factors independently, overlooking their coupled and dynamic impact across communication rounds. To address this limitation, we propose FedCSGA, a Client Selection method for Federated learning based on a Genetic Algorithm. FedCSGA encodes client subsets as chromosomes and evaluates these subsets through a non-linear fitness function that jointly integrates resource-aware metrics and history-aware participation weighting. This design captures subset-level interactions, mitigates persistent selection bias, and balances statistical utility with system efficiency. Extensive experiments on FMNIST, CIFAR-10, CIFAR- 100, and Tiny-ImageNet demonstrate that FedCSGA consistently outperforms state-of-the-art baselines across diverse heterogeneity settings, achieving accuracy improvements of up to 7.0% and faster convergence with fewer communication rounds. These results validate the effectiveness of jointly modeling statistical and system factors to achieve robust and efficient learning in heterogeneous federated environments.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.DC, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Distributed, Parallel, and Cluster Computing (cs.DC).
Pagina del lavoro: https://arxiv.org/abs/2611.04667 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T21:36:19.405Z dalla pagina https://arxiv.org/list/cs.DC/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).