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

作者：Anum Nawaz, Muhammad Irfan, Xianjia Yu, Hamad Aldawsari, Rayan Hamza Alsisi, Zhuo Zou, Tomi Westerlund · 发表于：IEEE Transactions on Consumer Electronics · 年份：2025 · DOI：10.1109/tce.2025.3620115 · 被引用次数：2 · 研究领域：Privacy-Preserving Technologies in Data、Mental Health Research Topics、Digital Mental Health Interventions

Federated learning (FL) is increasingly recognised for addressing security and privacy concerns in traditional cloud-centric machine learning (ML), particularly within personalised health monitoring such as wearable devices. By enabling global model training through localised policies, FL allows resource-constrained wearables to operate independently. However, conventional first-order FL approaches face several challenges in personalised model training due to the heterogeneous non-independent and identically distributed (non-iid) data by each individual’s unique physiology and usage patterns. Recently, second-order FL approaches maintain the stability and consistency of non-iid datasets while improving personalised model training. This study proposes and develops a verifiable and auditable optimised second-order FL frameworkBFEL (blockchain enhanced federated edge learning)based on optimised FedCurv for personalised 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 client device while effectively managing personalised training on non-iid and heterogeneous data. The incorporation of ethereum-based model aggregation ensures trust, verifiability, and auditability while public key encrypt...