BlockKD3A: A Web3-Native Framework for Decentralized Collaborative AI and User-Generated Model Assets

User-generated model assets in Web3 demand verifiable provenance, clear attribution, and dependable coordination across organizational boundaries without centralizing data. However, real-world deployments still face persistent challenges. Even algorithmically robust methods like KD3A often lack auditable coordination, failure resilience, and reproducible artifact lineage. These limitations make training brittle and results difficult to audit amidst node churn, adversarial behavior, and system heterogeneity. We introduce BlockKD3A, a hybrid on-chain/off-chain framework that operationalizes KD3A with three key system guarantees: (i) auditable coordination, achieved via smart contracts that record training state, model content identifiers (CIDs), and Consensus Focus (CF)–based attribution; (ii) reliability, through a transaction-safe client and failure-aware orchestration that applies nonnegative CF clipping, supports zero-CF fallback, and avoids blocking on stragglers; and (iii) reproducibility, enabled by content-addressable packaging and unified telemetry that couples machine learning metrics with blockchain events. In nine end-to-end deployments (40 epochs per target), BlockKD3A delivered near-centralized performance, 96.74% macro-average accuracy on Office-Caltech10 and 89.40% on DigitFive—substantially outperforming representative federated baselines. Coordination remained efficient and predictable, with only 13 blockchain transactions across all nine deployments, bounded gas costs per write (125,972-298,405), and stable epoch timings even under partial participation. By integrating KD3A's algorithm-native attribution with on-chain provenance and robust execution, BlockKD3A closes the gap between decentralized learning algorithms and reliable system deployment, providing auditability, predictable cost envelopes, and transferable engineering patterns for verifiable user-generated model assets.