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Our contribution is primarily engineering and architectural: a modular two\u2010channel design (provenance vs. update\u2010audit), lightweight on\u2010chain validation with off\u2010chain analytics, and a practical mapping to the 1\u2009+\u20095 architectural views. In a TensorFlow Federated\u2009+\u2009Hyperledger Fabric prototype with 10 clients, we observe \u224818% faster anomaly detection under attack and a\u2009+\u20090.4\u2009pp accuracy delta versus a baseline FL setup, with ~6% communication and ~8% energy overhead. We also provide a proof\u2010of\u2010concept zero\u2010knowledge succinct noninteractive argument of knowledge (zk\u2010SNARK) flow to validate per\u2010client summary properties off\u2010chain while anchoring results on\u2010chain. These contributions collectively advance the practical deployment of secure, auditable FL systems.<\/jats:p>","DOI":"10.1049\/sfw2\/6670439","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T14:34:51Z","timestamp":1764081291000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Blockchain\u2010Audited Federated Learning: Securing Data and Model Updates With On\u2010Chain 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