Federated Learning Incentive-Compatibility Evaluation for Computer Agriculture Data Cooperatives Under Elite Capture: An Artificial Intelligence Mechanism-Design Bound for Governance Legitimacy

Agricultural data cooperatives that pool farm-level information for machine-learning services face a governance problem that predates the algorithms: the members who control the pooling infrastructure can capture the surplus it generates. the present analysis treats federated learning as a mechanism and asks what incentive-compatibility bound a cooperative's governance rules must satisfy if elite capture is to be excluded rather than merely penalised after the fact. The analysis is theoretical. It formalises a cooperative as a coalition of heterogeneous producers who contribute private data to a shared model, specifies the information rents available to an internal elite that sets contribution rules and controls model deployment, and derives the conditions under which truthful contribution and non-capture are jointly incentive-compatible. The central result is a bound relating the elite's extractable rent to the marginal value of the model's accuracy, the observability of individual contributions, and the cost of exit. When contribution is unobservable and exit is costly, no budget-balanced transfer scheme implements the first-best; the second-best bound requires an external governance commitment that the cooperative itself cannot supply. The paper specifies that commitment as a mechanism-design constraint on the cooperative's charter and on the artificial-intelligence service contract, and identifies the conditions under which the bound is slack. The contribution is a governance-legitimacy criterion that can be checked before a cooperative adopts a federated learning service, rather than inferred from capture after it occurs.