- Review
48 Pages
Blockchain and machine learning (ML) are combined in systems that must learn from multi-party data and keep a verifiable record of what was used and decided. This scoping-style review of a ranked sample covers seven domains: healthcare, finance, supply chain, energy, smart contracts, transportation and education. We searched Scopus and IEEE Xplore on 15 October 2026, ranked 5195 candidate records, read 345 abstracts in rank order, coded a core corpus of 192 studies and read 38 in full. Federated learning is the most common method (93 of 192 core studies name it), and detection of fraud, intrusions and vulnerabilities is the most common task among the 38. None of the 38 reports a deployment, 24 report a measured ledger figure and two measure ledger energy. Ten use more than one privacy mechanism, and only two report accuracy with and without it. Attack tests are small, and one study tests a Sybil attacker. Studies differ too much to compare, so we do not rank domains. A ledger shows that a record has not changed, not that it was correct when written. The main gaps are realistic evaluation, shared benchmarks and reporting standards, and tested explanations for decisions made inside contracts.
Blockchains
5 November 2026


![Flow diagram of the search and selection process, adapted from PRISMA 2020 [18]. Blue boxes show screening stages; pink boxes indicate exclusions; gray boxes show records not screened or retained; and green boxes represent included studies. Colored sidebars identify the review phases. Downward arrows show progression, while rightward arrows indicate records removed or set aside. The core corpus comprises 192 studies, with 38 full texts examined (36 core and 2 additional studies).](https://mdpi-res.com/blockchains/blockchains-04-00023/article_deploy/html/images/blockchains-04-00023-g001-550.jpg)




![Classification of federated learning in [10]. (a) Horizontal federated learning (HFL), partitioned by samples. (b) Vertical federated learning (VFL), partitioned by features. (c) Federated transfer learning (FTL).](https://mdpi-res.com/blockchains/blockchains-02-00003/article_deploy/html/images/blockchains-02-00003-g001-550.jpg)

