Efficient design of blockchain-based electronic health record systems with federated learning, off-chain storage, and zk-rollup parallelism for minimizing transaction costs

The paper titled "Efficient design of blockchain-based electronic health record systems with federated learning, off-chain storage, and zk-rollup parallelism for minimizing transaction costs" introduces a novel model called FZRP (Federated-ZK-Rollup Pipeline). This model aims to address the impracticality of full on-chain storage for EHRs on blockchain due to high storage costs and limited throughput.

The proposed FZRP model combines Federated Learning, off-chain storage using IPFS, zk-rollup batching with adaptive batch sizing, and parallel proof pipelines. These techniques work together to significantly reduce the per-record transaction cost while ensuring auditability and privacy of the EHRs. The model's effectiveness is demonstrated through a synthetic dataset of 50,000 EHRs, showing orders-of-magnitude cost savings under realistic assumptions.

The authors provide a formal cost model, analyze latency and security aspects, and conduct sensitivity studies to validate the model's performance. Experimental results reveal that adaptive batching can reduce per-record transaction costs to as low as $0.000024, achieving over 99.999% cost reduction compared to full on-chain storage. This approach strikes a balance between scalability, privacy, and cost-effectiveness, making it a promising solution for blockchain-based EHR systems.