# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 3 AI-Driven Blockchain Solutions
### AI-Driven Privacy Protection
#### 3.3.2 Federated-Learning-Based Privacy Protection for Blockchain
[183] proposed UBFL, an innovative privacy-preserving federated learning mechanism that integrates blockchain technology for secure and efficient data sharing. Unlike traditional methods relying on differential privacy, UBFL employs adaptive nonlinear encryption to safeguard the privacy of UAV model updates while ensuring data integrity and accuracy. [PERSON] et al. [184] proposed a consortium blockchain (CB) and FL-based data management framework for medical network communities, enabling secure data sharing between medical institutions and research organizations. [PERSON] et al. [185] proposed a blockchain-based federated learning approach for privacy-preserving sharing of non-IID data in the industrial internet. This approach dynamically updates local models to mitigate the accuracy degradation caused by non-IID data. [PERSON] et al. [186] proposed a blockchain-based hierarchical federated learning framework designed to enhance the training of non-IID data, improve data privacy protection, and optimize federated learning performance. [PERSON] et al. [187] introduced an innovative framework leveraging blockchain-based federated learning to identify 15 different lung diseases while ensuring data privacy and security. [PERSON] et al. [188] proposed an FL framework integrating multiple machine learning models to prevent fraudulent transactions in blockchain systems while preserving customer privacy, eliminating the need for private customer data exchange between institutions. [PERSON] et al. [189] proposed a blockchain-based machine learning scheme for secure data sharing in distributed energy storage networks, incorporating secure federated learning to formulate data sharing as a machine learning problem. [PERSON] et al. [190] proposed an innovative framework that integrates Trusted Execution Environment and blockchain technology to address data security and privacy challenges in IoT supply chain management. [PERSON] et al. [191] proposed a multi-task privacy-preserving FL framework using partitioned blockchain, enabling multiple requesters to execute different FL tasks concurrently. [PERSON] et al. [192] proposed a blockchain-based pseudorandom number generation (BPNG) protocol using verifiable random functions (VRFs) to ensure fairness in federated learning schemes. [PERSON] et al. [193] proposed a benchmark evaluation approach to optimize federated person re-identification performance in blockchain-powered edge-intelligent UAV delivery systems. [PERSON] et al. [194] integrated blockchain-reinforced federated learning for post-COVID-19 patient data in IoMT applications to enhance data privacy and security. [PERSON] et al. [195] modeled multi-source devices using federated learning and stored model parameters and reputation values on the blockchain to enhance training accuracy and ensure data privacy security. As shown in Tables 24 and 25, this research area explores blockchain-native privacy-preserving mechanisms that leverage zero-knowledge proofs, access control encryption, and knowledge graphs to enable decentralized data sharing, financial credit modeling, and secure transactions without relying on federated learning. These techniques emphasize decentralization, tamper-proof transparency, and verifiable transactions while addressing data privacy through cryptographic methods. However, practical deployment faces challenges due to high computational and storage overheads, particularly requiring strong computing power nodes. Complementary AI-driven approaches integrating zero-knowledge proofs, homomorphic encryption, and ring signatures aim to enhance privacy in blockchainsystems yet suffer from increased computational complexity that limits applicability on resource-constrained devices. The current integration of AI with these privacy mechanisms often remains loosely coupled, missing deeper synergistic optimizations. Future directions focus on developing lightweight AI models specifically optimized for accelerating ZKP/FHE computations and leveraging AI-generated constraints within zero-knowledge proof systems.