# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Privacy Protection
#### 3.3.1 Other Blockchain-Based Privacy Protection Mechanisms  
Traditional blockchain-based privacy protection mainly uses encryption and zero-knowledge proofs to encrypt user data without incorporating federated learning, and many scholars have carried out related research. [PERSON] et al. proposed [171] an Artificial-Intelligence-based Lightweight Blockchain Security Model (AILBSM) for ensuring privacy and security of IIoT systems. This novel model aims to address security and privacy issues that may arise when dealing with cloud-based IIoT systems that process data in the cloud or at the edge of the network (on devices). [PERSON] et al. [172] proposed a novel solution called Hierarchical Blockchain-Enabled Cloud Edge Choreography Federated Learning (HBCE-FL) to address these challenges. HBCE-FL aims to provide secure, intelligent, and distributed data analysis for IoT users. [PERSON] et al. [173] studied how data sharing is achieved through model sharing, and, based on this, a secure data sharing mechanism called BP2P-FL was proposed, which employs peer-to-peer federated learning and protects the privacy of data providers. [PERSON] et al. [174] proposed BV-ICV, a blockchain-enabled and privacy-preserving FL framework for ICVs in edge-envisioned V2X environments, to alleviate these concerns. The system uses zero-knowledge succinct non-interactive knowledge argument (zkSNARK) validation compiled into smart contracts to prevent malicious, compromised, or even justified ICVs from uploading unreliable, incorrect, or low-quality model updates. [PERSON] et al. [175] proposed an interpretable federated learning approach with controlled machine learning efficiency and controlled credit model decision-making, leading to controlled credit model complexity and transparently traceable credit decision-making mechanisms. They then proposed an interpretable federated learning training mechanism for credit data to prevent model gradient leakage trained by a single node throughout the model training period. [PERSON] et al. [176] introduced the concept of distributed edge intelligence that combines the advantages of FL, differential privacy (DP), and blockchain to address the previously posed problem. [PERSON] et al. [177] proposed a blockchain-enabled federated learning (BeFL) for distributed cybersecurity malicious behaviour knowledge base architecture to ensure the security of knowledge transfer.  
As shown in Table 23, the research on integrating FL with blockchain technology aims to enhance data privacy and security in decentralized data processing across domains like healthcare, IoT, IoV, finance, and supply chains. Blockchain enables decentralized governance, secure data storage, and model validation, while FL facilitates collaborative model training without raw data sharing, mitigating leakage risks. Techniques such as differential privacy, homomorphic encryption (HE), and trusted execution environments (TEEs) are widely employed to strengthen privacy, complemented by smart contracts and reputation mechanisms to ensure system fairness and trustworthiness. However, current approaches often overlook the threat of malicious nodes injecting poisoned models, compromising collaborative learning integrity, and suffer from communication overheads that hinder performance in bandwidth- or latency-constrained environments. Moreover, the applicability of these methods remains limited to data-sensitive distributed scenarios, with algorithmic research facing challenges in scalability and accuracy. Future advancements should prioritize robust anomaly detection mechanisms to filter malicious updates and leverage reinforcement learning to dynamically optimize model synchronization intervals, thereby improving system resilience, communication efficiency, and broader applicability.