Abstract: To address challenges in cross-domain collaboration posed by data privacy and compliance constraints, Federated Learning (FL) integrated with blockchain mitigates centralization risks in traditional FL, yet existing solutions face insufficient model update quality assessment and validator trust crises. This paper introduces a decentralized blockchain-based federated learning framework. It features a dynamic closed-loop system that coordinates quality, trust, and equity. It works by:1)Validator Quality Score,quantifies validator performance using multi-round cross-validation and spatiotemporal weighting, converting quality scores into dynamic voting weights to suppress collusion attacks;2)Model Quality Factor,tracks worker nodes' historical contributions via sliding windows and dynamically adjusts update thresholds using validator accuracy to distinguish high-value updates from malicious perturbations; 3)Model Quality-Driven Dynamic Proof-of-Stake,binds node stakes to contribution quality,ensuring high-stake nodes deliver high-quality outputs.The framework is tested on multiple datasets. Its synergistic mechanisms maintain strong performance under malicious attacks in Non-IID environments. Results show a 12.5% average accuracy gain over baselines. Defense effectiveness on CIFAR-10 improves by up to 38%. The system suppresses malicious nodes' stake to only 1%, far below the 13% baseline level. Communication costs remain comparable. This method successfully solves the consistency problem between model quality and validator performance.

摘要： 针对数据隐私与合规约束制约跨域协同的问题，联邦学习（FL）与区块链融合虽能缓解传统FL的中心化风险，但现有方案仍面临模型更新质量评估不足与验证节点可信危机等挑战。本研究提出一种去中心化区块链联邦学习框架，其核心创新在于构建了一个质量-信任-权益协同的动态闭环系统，具体表现为：1)验证者质量评分，采用多轮次交叉验证与时空权重算法量化验证者表现，将质量评分转化为动态投票权重以抑制共谋攻击；2)模型质量因子，基于滑动窗口追溯工人节点历史贡献，结合验证者准确率动态调整更新阈值，区分高价值更新与恶意扰动；3)模型质量驱动的动态权益证明机制，将节点权益与贡献质量绑定，确保高权益节点具备高质量输出。在多个数据集上的实验表明，该框架通过上述机制的协同作用，在非独立同分布场景下遭遇恶意节点干扰时，模型精度相较于基线方法平均提升12.5%，在CIFAR-10等复杂数据集上对特定攻击的防御效果提升高达38%，恶意节点的权益累计占比被成功压制至约1%，远低于基线方案的13%。同时，通信开销与基线相当，有效解决了模型更新质量与验证者表现的一致性问题。