DI-Flame: A Decentralized Incentive Mechanism for Federated Learning Model Validation

Abstract

Federated learning enables multiple data owners to collaboratively train a global model, but verifying the correctness of submitted updates remains a critical challenge. Malicious clients may poison model weights, and colluding validators can undermine aggregation. We propose DI-FLAME, a decentralized validation framework that introduces a stake-and-proof mechanism for model verification. Each claim is submitted with a justification and stake, evaluated via a black-box credibility function. Validators engage in peer review, and contradictors may challenge weak claims by submitting stronger proofs and higher stakes. Rewards and penalties are dynamically distributed based on justification strength and challenge outcomes. DIFLAME provides robust defense against poisoned updates, even under adversarial majorities. While blockchain infrastructure is not required, DI-FLAME is compatible with with decentralized ledgers, enabling transparent recording of validation outcomes when deployed over a blockchain.