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PoCoFL: POlicy-COmpliant Federated Learning — arXiv:2610.03650

Source: arXiv
Original: https://arxiv.org/abs/2610.03650
License (as archived): CC0-1.0
Archived: 2026-10-05T23:06:27.435Z
SHA-256 of the archived text: efa84d1c552a477d83ea9b8a8e75a3b89152d00b127eff637f02754120ce0b1c
Stable URL of this page: https://metraipolis.com/documento/dc_09031fca1ece

How to cite

PoCoFL: POlicy-COmpliant Federated Learning — arXiv:2610.03650 — arXiv, https://arxiv.org/abs/2610.03650 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_09031fca1ece (Metraipolis archive dc_09031fca1ece, sha256 efa84d1c552a477d83ea9b8a8e75a3b89152d00b127eff637f02754120ce0b1c)

BibTeX

@misc{dc09031fca1ece,
  title = {PoCoFL: POlicy-COmpliant Federated Learning — arXiv:2610.03650},
  howpublished = {arXiv},
  url = {https://arxiv.org/abs/2610.03650},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_09031fca1ece; sha256 efa84d1c552a477d83ea9b8a8e75a3b89152d00b127eff637f02754120ce0b1c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv, https://arxiv.org/abs/2610.03650. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Federated Learning (FL) is a privacy-oriented learning paradigm that enables collaborative model training while keeping training data local to participating clients. However, it does not guarantee that clients submit policy-compliant contributions or that aggregators process admitted contributions correctly. Existing verifiable FL systems tailor validation rules to specific FL settings, learning workflows, and cryptographic constructions, limiting their applicability across network topologies, participant roles, and aggregation semantics. In this paper, we present PoCoFL, a policy-compliant federated learning framework that separates three aspects: (i) FL type, (ii) policy semantics, and (iii) cryptographic realisation. We provide a formalisation that captures client and aggregation requirements as policy-dependent relations. Clients prove compliance of their contributions using commitments and non-interactive zero-knowledge proofs, while aggregators prove that the recorded set of admitted contributions was processed according to the selected aggregation policy. We demonstrate PoCoFL through four formal instantiations: (i) vanilla, (ii) continual, (iii) personalised, and (iv) threshold-encrypted federated learning. We evaluate the effects of policy enforcement on the learning objectives of vanilla, personalised, and continual FL. We further implement proof-of-concept realisations of all four instantiations, demonstrating the versatility and practical feasibility of PoCoFL. Overall, these results show that PoCoFL can capture complex policy representations while remaining network-topology agnostic.

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR) ; Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.03650 — 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 2026-10-05T23:01:08.551Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).