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Local Epochs, Averaging, and Variable Selection in Federated Lasso — arXiv:2609.17685

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.17685
License (as archived): CC0-1.0
Archived: 2026-10-15T15:40:04.663Z
SHA-256 of the archived text: 3029d0be27fea9d160bab5f480ddd9d28ea35aa31cff6549cf1fa0ec1691a8b5
Stable URL of this page: https://metraipolis.com/documento/dc_1007d1fdc3cb

How to cite

Local Epochs, Averaging, and Variable Selection in Federated Lasso — arXiv:2609.17685 — arXiv (en), https://arxiv.org/abs/2609.17685 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_1007d1fdc3cb (Metraipolis archive dc_1007d1fdc3cb, sha256 3029d0be27fea9d160bab5f480ddd9d28ea35aa31cff6549cf1fa0ec1691a8b5)

BibTeX

@misc{dc1007d1fdc3cb,
  title = {Local Epochs, Averaging, and Variable Selection in Federated Lasso — arXiv:2609.17685},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.17685},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_1007d1fdc3cb; sha256 3029d0be27fea9d160bab5f480ddd9d28ea35aa31cff6549cf1fa0ec1691a8b5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.17685. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Theoretical analysis and Monte Carlo simulation separate local-epoch effects from averaging and tuning effects in federated Lasso. How much local work should precede averaging when fitting a sparse regression? An orthogonal calculation shows that extra epochs can have no effect while averaging still enlarges the selected set. A correlated two-site construction gives an exact, nonmonotone limiting objective gap and its minimizing epoch count. We then compare coordinate-descent averaging, two thresholding modifications, and adapted FedDualAvg across twelve scenarios and 600 replicates. Methods share a penalty, independent validation samples, selection rules, and resource limits. FedDualAvg generally achieves smaller objective gaps but does not always recover variables better. Thresholding gains depend strongly on selection rules. Epoch effects vary with correlation, signal strength, site allocation, and the outcome measured. These results distinguish faster iteration from better optimization, prediction, and variable selection.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-17 in stat.CO).
Categorie arXiv: Computation (stat.CO).
Pagina del lavoro: https://arxiv.org/abs/2609.17685 — 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 2026-10-15T15:08:06.743Z dalla pagina https://arxiv.org/catchup/stat.CO/2026-09-17?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).