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Fed-IGTD: A federated learning-based framework for tabular-to-image encoding — arXiv:2610.39008

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.39008
License (as archived): CC0-1.0
Archived: 2026-11-01T02:24:57.792Z
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How to cite

Fed-IGTD: A federated learning-based framework for tabular-to-image encoding — arXiv:2610.39008 — arXiv (en), https://arxiv.org/abs/2610.39008 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f63191f8dcda (Metraipolis archive dc_f63191f8dcda, sha256 cc2c6e0134315a11074ad2e62fb6340b4cca8ea5d0a349172eb362f33faadf30)

BibTeX

@misc{dcf63191f8dcda,
  title = {Fed-IGTD: A federated learning-based framework for tabular-to-image encoding — arXiv:2610.39008},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.39008},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f63191f8dcda; sha256 cc2c6e0134315a11074ad2e62fb6340b4cca8ea5d0a349172eb362f33faadf30},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.39008. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Tabular-to-image encoding methods, which transform tabular data into image-like representations, have shown promising results in predictive tasks. Despite their benefits, these methods follow a centralized paradigm, in which the data are collected and models are trained on a central device. This raises privacy and security concerns, particularly the risk of data leakage, unauthorized data collection and access to users' private information. Federated Learning (FL) provides a decentralized framework in which multiple clients jointly optimize a shared model without directly sharing training data. We introduce Fed-IGTD, a horizontal FL-based approach in which clients share a common feature space and upload only local statistics, from which the server reconstructs the global correlation matrix and obtains a feature-to-pixel mapping to train a convolutional neural network via Federated Averaging (FedAvg). We studied two differential privacy (DP) mechanisms: (i) a one-shot Gaussian mechanism protecting the correlation-aggregation step; and (ii) a client-level DP-FedAvg variant protecting the training with privacy loss composed via a Renyi-DP accountant. We evaluated Fed-IGTD on five datasets under a systematic sweep of non-IID severity and privacy budgets. Privacy protection applied to the mapping-construction step has little impact on predictive performance. By contrast, DP-FedAvg substantially degrades utility at moderate privacy budgets with a small client cohort (K=5). Ablation study indicates that this degradation is driven mainly by the injected noise rather than by uniform averaging or update clipping. Macro-F1 exhibits sensitivity to performance degradation than accuracy under non-IID heterogeneity and class imbalance.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2610.39008 — 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 2026-11-01T02:12:44.147Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).