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Byzantine-tolerant distributed learning of finite mixture models under partial corruptions — arXiv:2609.11309

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.11309
License (as archived): CC0-1.0
Archived: 2026-10-16T05:59:47.235Z
SHA-256 of the archived text: 2f2ce71082e5289b9f42985dba189eb87f25f7056c810f230807f92e230f7084
Stable URL of this page: https://metraipolis.com/documento/dc_2bc01f794dcf

How to cite

Byzantine-tolerant distributed learning of finite mixture models under partial corruptions — arXiv:2609.11309 — arXiv (en), https://arxiv.org/abs/2609.11309 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bc01f794dcf (Metraipolis archive dc_2bc01f794dcf, sha256 2f2ce71082e5289b9f42985dba189eb87f25f7056c810f230807f92e230f7084)

BibTeX

@misc{dc2bc01f794dcf,
  title = {Byzantine-tolerant distributed learning of finite mixture models under partial corruptions — arXiv:2609.11309},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.11309},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bc01f794dcf; sha256 2f2ce71082e5289b9f42985dba189eb87f25f7056c810f230807f92e230f7084},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.11309. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Finite mixture models characterize heterogeneous populations and are increasingly fitted to distributed data using split-and-conquer procedures that aggregate local mixture estimates at a central server. The aggregation step can be seriously compromised when transmitted local mixture estimates are partially or completely corrupted. To guard against Byzantine failures, existing robust aggregation methods have been developed for settings in which a local mixture estimate is either entirely authentic or entirely corrupted. Such methods can discard useful information when only some component estimates are corrupted. We consider component-wise Byzantine failure, in which some component estimates may be corrupted, but for each mixture component, a majority of the corresponding local estimates remain authentic. We propose component-wise filtered mixture reduction (CFMR), which selects a data-driven anchor, aligns transmitted components, filters each aligned cluster by a majority radius, and aggregates the retained estimates through mixture reduction. By filtering out only unreliable components, CFMR preserves authentic information from partially corrupted machines without requiring knowledge of the failure rates. We establish an adaptive convergence bound for CFMR and show that, under suitable conditions, it attains the oracle rate that would be achieved if the authentic component estimates were known in advance. Simulations and a real-data application show that CFMR remains close to the component-level oracle, whereas whole-machine filtering and unprotected aggregation can deteriorate substantially.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-11 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2609.11309 — 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 2026-10-16T05:49:22.703Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).