Blockchain-Integrated Edge-Cloud-Enabled Healthcare Data Analytics Based on Distributed Federated Learning and Deep Neural Networks
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The exponential growth of healthcare Internet of Things (IoT) data necessitates secure, low-latency analytics that extend beyond centralized architectures. This paper presents BDAFL DNN, a blockchain-integrated data analytics framework that combines Federated Learning (FL) and Deep Neural Networks (DNNs) for real-time, privacy-preserving healthcare analytics across edge and cloud resources. Local devices such as smartwatches and phones collect noninvasive time series sensor streams (heart rate, temperature, and abdomen sensors), perform on device DNN training, and send only model updates to healthcare edge nodes, where a blockchain ledger validates updates for integrity and traceability; validated updates are then aggregated in the cloud via FL to produce a global model without sharing raw data. In a simulation study against representative baselines, BDAFL DNN reduced execution time, energy use, and resource consumption, lowered the deadline miss ratio, and improved blockchain validation correctness. These results show that integrating blockchain with FL-driven edge and cloud DNN analytics can deliver scalable, secure, and timely insights for future healthcare IoT systems.
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