Incentivizing Federated Learning: Agreement is enough

Federated learning enables collaborative training on private data, but attracting clients with valuable data requires incentives for effortful training and honest reporting. Computing these rewards must be inexpensive enough for every training round. Knowledge-Free Correlated Agreement (KFCA) addresses this need with fixed Dasgupta-Ghosh agreement-minus-chance scoring, avoiding correlation estimation and penalizing unilateral label flips under categorical and task assumptions. Federated LLM tuning, industrial inspection, and runtime comparisons assess rewards and efficiency; a smart-contract prototype illustrates auditable execution.