A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Blockchain-Enabled Privacy-Preserving Second-Order Federated Edge Learning in Personalised Healthcare
Tekijät: Nawaz, Anum; Irfan, Muhammad; Yu, Xianjia; Aldawsari, Hamad; Alsisi, Rayan Hamza; Zou, Zhuo; Westerlund, Tomi
Kustantaja: Institute of Electrical and Electronics Engineers (IEEE)
Julkaisuvuosi: 2025
Lehti:: IEEE Transactions on Consumer Electronics
ISSN: 0098-3063
eISSN: 1558-4127
DOI: https://doi.org/10.1109/TCE.2025.3620115
Verkko-osoite: https://doi.org/10.1109/tce.2025.3620115
Tiivistelmä
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 framework BFEL (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 encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework’s high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
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 framework BFEL (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 encryption enhances privacy and security. Experimental results of federated CNNs and MLPs utilizing mnist, cifar-10, and PathMnist demonstrate framework’s high efficiency, scalability, suitability for edge deployment on wearables, and significant reduction in communication cost.
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