Blockchain-Enhanced Federated Learning for Secure IoMT Data Training

The integration of blockchain technology and Federated Learning (FL) is being deployed to address critical privacy, security, and data integrity challenges within the Internet of Medical Things (IoMT). Recent research highlights a shift toward decentralized frameworks that allow medical data to remain on local devices, mitigating the risks associated with centralized data transmission and single-point failures.

In Smart Healthcare 5.0, the adoption of IoMT has introduced pressing concerns regarding real-time threat detection and parameter breaches. Traditional FL models often rely on a central server for aggregation, which can be vulnerable to poisoning attacks, inference risks, and system-wide failures if the server is compromised.

Split Federated Learning and Blockchain Integration

To counter these vulnerabilities, a novel framework combining Split Federated Learning (SFL) with blockchain has been developed. SFL improves security by dividing the deep learning model between the IoMT and Edge layers. In this architecture, lightweight Convolutional Neural Networks (CNN) at the IoT layer extract spatial features from patient data, while Edge-based Bidirectional Long Short-Term Memory (BiLSTM) networks are used to identify temporal patterns and threats.

Blockchain serves as the trust mechanism for this decentralized training process. By utilizing the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, the framework ensures that the system remains tamper-proof and maintains authentication and integrity across the network.

Experimental results on ToN-IoT and IoT healthcare datasets indicate that this approach achieves an accuracy of 99.95%. Technical performance metrics show a block commit rate of 450 b/s and a reduced consensus time of 250 ms, enhancing the framework’s viability as a real-time intrusion detection solution.

Customized Federated Learning for Diverse Patient Needs

Beyond general security, researchers are addressing the challenge of diverse patient conditions through blockchain-driven customized federated learning. Because patient needs vary widely, standard AI approaches are often insufficient for personalized healthcare.

This customized framework allows clients to train models tailored to specific needs without exposing raw data to central servers. The system incorporates Edge Computing gateway devices to handle data collection and preprocessing, which improves overall data efficiency. A specific model partitioning method is employed to enable collaborative training across IoMT devices while strictly preserving patient privacy.

The reliability of this customized approach has been validated using 2D Colon Pathology, Breast Tumor, and CIFAR-10 datasets. These evaluations demonstrate that the framework adheres to established security and privacy standards while remaining resilient against external threats.

Addressing Resource Limitations and Data Noise

The synergy between blockchain and FL also addresses the physical and computational limitations of IoMT devices. As data sharing grows, resource constraints and the presence of noise in medical data become significant hurdles.

Recent developments include the use of data cleaning as an initial process to remove noise before the federated learning model is trained, ensuring higher quality inputs for the AI. Adaptive blockchain frameworks are being explored that utilize reinforcement learning-based consensus and resource forecasting to optimize how the network handles the computational load of medical data.

These combined technologies aim to transform the medical field by providing a secure environment for AI-driven diagnosis and rehabilitation, moving away from risky centralized architectures toward a robust, decentralized ecosystem.