Federated learning (FL) enables collaborative model training without data centralization but struggles with incentive alignment, accountability, and robustness under heterogeneous non-IID data. Existing decentralized FL (DeFL) systems leverage blockchains for coordination yet face high on-chain overhead, unverifiable self-reports, and weak fairness guarantees. We present the FLAI Protocol, a trusted execution environment (TEE)-backed DeFL framework that integrates enclave-validated aggregation, sparse Peer Evaluation Graph (sPEG) scoring, and on-chain settlement. sPEG incorporates peer-based quality, class coverage, promptness, effort, and reputation into composite contribution scores. The protocol formalizes contribution evaluation and tokenized incentive distribution, ensuring verifiable fairness and resilience against free-riding and collusion. We evaluate FLAI on image, text, and healthcare datasets and demonstrate parity with FedAvg in utility while improving fairness and accountability. A case study in consumer eHealth (aggregating wearable and electronic health record data for real-world evidence generation) illustrates the protocol's applicability to privacy-critical domains where equitable rewards, auditability, and data sovereignty are essential.

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