A data factor market trading mechanism based on federated learning and blockchain

With the accelerated marketization of data factors, achieving fair contribution evaluation, privacy-preserving verification, and dynamic incentives in decentralized environments has emerged as a critical challenge. Existing studies exhibit a structural tension between privacy protection and verification transparency, while lacking adaptive mechanisms for non-independent and identically distributed (Non-IID) data scenarios. To address these issues, this paper proposes a collaborative trading framework integrating zero-knowledge proofs, personalized federated learning, and reinforcement learning. The framework employs zk-SNARKs to construct non-interactive proofs, thereby resolving the verification-privacy dilemma. A meta-learning–driven personalized aggregation scheme is introduced to correct valuation bias under Non-IID data distributions, and a deep Q-network (DQN) agent is deployed to enable dynamic incentive responses to market supply–demand fluctuations. Experiments conducted on Ethereum and Farcaster datasets demonstrate that the proposed mechanism improves the Contribution Fairness Index (CFI) by 19.7%–22.4% over the strongest baseline, achieving a Verification-Utility Ratio (VER) of 24.6. Under a collaboration scale of N = 20, market vitality entropy increases to 0.75 (baseline: 0.41), effectively suppressing monopolistic tendencies. Moreover, despite the introduction of proof mechanisms, the estimated additional on-chain verification and consensus latency per round is approximately 13 s, calibrated against empirical benchmarks. This work provides a verifiable trading mechanism for data factor markets that jointly ensures privacy, fairness, and efficiency, supporting secure data circulation in domains such as healthcare and finance.