Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalized Healthcare

2025-05-31

AI Summary

To address severe model drift, weak personalization capability, untrustworthy aggregation, and inadequate privacy protection arising from non-IID data across edge devices in personalized healthcare, this paper proposes Blockchain-empowered Federated Edge Learning (BFEL). BFEL innovatively integrates FedCurv—a second-order optimization method leveraging the Fisher Information Matrix—with Ethereum smart contracts to enable parameter-importance-aware aggregation, model drift suppression, and on-chain verifiable auditing. Communication privacy is ensured via RSA/PKE encryption. Extensive evaluations on MNIST, CIFAR-10, and PathMNIST demonstrate significant improvements in personalized accuracy, a 37% reduction in convergence rounds, and scalable support for hundreds of edge nodes. BFEL thus fulfills stringent privacy-preserving and regulatory-compliant auditing requirements essential for real-world medical applications.

Technology Category

• Machine Learning: Privacy
• Search and Optimization: Learning to Search
• Computer Vision: Bias, Fairness & Privacy

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Blockchains and distributed ledgers
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Federated learning (FL) has attracted increasing attention to mitigate security and privacy challenges in traditional cloud-centric machine learning models specifically in healthcare ecosystems. FL methodologies enable the training of global models through localized policies, allowing independent operations at the edge clients' level. Conventional first-order FL approaches face several challenges in personalized model training due to heterogeneous non-independent and identically distributed (non-iid) data of each edge client. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalized model training. This study proposes and develops a verifiable and auditable optimized second-order FL framework BFEL (blockchain-enhanced federated edge learning) based on optimized FedCurv for personalized healthcare systems. FedCurv incorporates information about the importance of each parameter to each client's task (through Fisher Information Matrix) which helps to preserve client-specific knowledge and reduce model drift during aggregation. Moreover, it minimizes communication rounds required to achieve a target precision convergence for each edge client while effectively managing personalized training on non-iid and heterogeneous data. The incorporation of Ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing Mnist, Cifar-10, and PathMnist demonstrate the high efficiency and scalability of the proposed framework.

Problem

• Enhances privacy in federated learning for healthcare using blockchain
• Improves personalized model training with second-order FL on non-iid data
• Reduces communication rounds and ensures verifiability via Ethereum-based aggregation

Innovation

• Blockchain-enhanced second-order federated learning
• Optimized FedCurv for personalized healthcare
• Ethereum-based secure model aggregation