Blockchain-Governed Federated Intelligence: A Formal Model for Trust-Aware and Auditable Collaborative Learning

The joint artificial intelligence across cross-organizational digital ecosystems requires coordination strategies to maintain reliability where one of the organizations has no central, trusted controller. Although federated learning requires minimal exposure to raw data, its effectiveness in open and heterogeneous systems is diminished by strategic behavior and lack of incentive convergence and the possibility of introducing bad or low-quality updates. This paper in turn presents Blockchain -Governed Federated Intelligence (BGFI), a model-oriented architecture that integrates onto-chain governance, policy compliance, security filtering and trustweighted aggregation into a unified system of state-transition. BGFI directly incorporates checking results, reputation interaction, and integrity futility indicators into the aggregation pipeline, which enables one to perform formal logic with respect to protocol compliance and allowable influence. Within the given assumptions, we show that the model evolution is bounded in a round-by-round term and that updates adopted are publicly verifiable; in addition, we give an analytical explanation of coordination cost, on-chain storage growth and latency in terms of round-by-round basis. To demonstrate how the framework can be operationalized in any multi-stakeholder system, two real-world application cases are outlined on which the framework can be applied in smart transportation and decentralized energy systems. The future research direction is earmarked to empirical benchmarking, the main role of this manuscript is the provision of a formally defined, practitionable model and an overall overview of blockchain-controlled federated intelligence.