用信任分激励联邦学习中诚实参与，防作弊。
Trust-Based Incentive Mechanisms in Semi-Decentralized Federated Learning Systems
- 根据数据质量、模型准确率等动态评估节点信任分。
- 高信任节点获得更多训练机会，低信任者受惩罚。
- 结合区块链自动执行，提升系统透明与去中心化。
在联邦学习（FL）中，去中心化训练使多个参与者协作优化共享机器学习模型，而无需交换原始数据。然而，存在潜在恶意或故障节点可能降低模型性能，威胁系统完整性。本文提出一种新型基于信任的激励机制，通过动态评估节点的数据质量、模型准确率、一致性及贡献频率等指标，衡量其可信度。高信任节点将获得更多参与机会，低信任者则受到惩罚。此外，我们探索结合区块链与智能合约，实现信任评估和激励分配的自动化，保障过程透明且去中心化。该理论框架旨在构建更稳健、公平、透明的联邦学习生态，降低不可信参与者带来的风险。
原文摘要 · Abstract (English)
In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data. However, ensuring the integrity and reliability of the system is challenging due to the presence of potentially malicious or faulty nodes that can degrade the model's performance. This paper proposes a novel trust-based incentive mechanism designed to evaluate and reward the quality of contributions in FL systems. By dynamically assessing trust scores based on fac-tors such as data quality, model accuracy, consistency, and contribution fre-quency, the system encourages honest participation and penalizes unreliable or malicious behavior. These trust scores form the basis of an incentive mechanism that rewards high-trust nodes with greater participation opportunities and penal-ties for low-trust participants. We further explore the integration of blockchain technology and smart contracts to automate the trust evaluation and incentive distribution processes, ensuring transparency and decentralization. Our proposed theoretical framework aims to create a more robust, fair, and transparent FL eco-system, reducing the risks posed by untrustworthy participants.
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