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Blockchain technology, renowned for its immutability, transparency, and decentralized nature, can provide tamper-evident provenance and auditable update trails that improve model trustworthiness when integrated carefully with federated learning (FL) workflows. We present a concrete, implementable hybrid architecture that integrates a permissioned blockchain (Hyperledger Fabric) with TensorFlow Federated to record per-sample and per-update provenance via Merkle-tree hashing and compact on-chain metadata. Our contribution includes (i) a gas-\/storage-efficient on-chain metadata layout and Merkle-backed transaction format, (ii) a smart-contract-based anomaly-scoring verification routine for gradient filtering, and (iii) an analysis of PBFT-based validator sizing tuned for iterative FL rounds. We implemented the framework in a 10-client FL experiment (TensorFlow Federated\u2009+\u2009Hyperledger Fabric) on MNIST. Empirically, our system detected poisoned updates 18% faster than a baseline FL pipeline, increased final model accuracy from 97.1 to 97.5% (+\u20090.4%) under targeted poisoning, and incurred modest overheads (+\u20096% communication,\u2009+\u20098% energy). Implementation details include SHA-256 hashing, Merkle-tree batch commitments, PBFT consensus on a permissioned validator set, and off-chain storage of raw payloads (IPFS) with on-chain hashes. Our results demonstrate that careful co-design of on-chain provenance, compact metadata, and smart-contract audits can substantially increase FL robustness against poisoning and Sybil attacks while keeping latency and energy costs within practical bounds for consortium deployments.<\/jats:p>","DOI":"10.1007\/s44163-026-01443-5","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:02:57Z","timestamp":1779688977000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Trustworthy AI for secure and robust machine learning through blockchain enabled data integrity"],"prefix":"10.1007","volume":"6","author":[{"given":"Seid Mehammed","family":"Abdu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Girma","family":"Bewuketu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Demeke","family":"Getaneh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md Nasre","family":"Alam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armilyn Cosico","family":"Fernandez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna Beth Amante","family":"Basco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zafrul","family":"Hasan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad","family":"Serajuddin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"1443_CR1","unstructured":"Goh E, Kim D-Y, Lee K, Oh S, Chae J-E, Kim D-Y. 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