Clustered Federated Learning for Healthcare Analytics: A Dual Blockchain-Functional Encryption Method for Fortified Aggregation

The use of healthcare data for collaborative machine learning training amplifies the demand for strong privacy and security protection. Federated learning (FL) addresses this by sharing model gradients instead of raw patient data. However, FL faces critical challenges, including vulnerability to adversarial attacks (e.g., membership inference, model poisoning), reliance on centralized aggregation (introducing single points of failure), and privacy leakage risks from gradient exchanges. To overcome these limitations, we propose C2SecFL, a cross-clustered secure FL framework that combines adaptive, model-aware clustering with an inner-product functional-encryption (IPFE) scheme enabling encrypted aggregation without a key distribution center (KDC). A lightweight permissioned blockchain provides tamper-evident coordination and auditability across clusters without introducing a centralized trust anchor. Experiments on diverse models show substantially lower cryptographic overhead compared to homomorphic-encryption baselines. Encryption is approximately 3.6× faster, and aggregation is up to 2.2× faster, while predictive utility is preserved. Under adversarial settings with compromised participants, C2SecFL reduces the average attack success rate by about 80% relative to a FedAvg baseline. It mitigates membership-inference risk by encrypting updates end-to-end and revealing only aggregate inner products. Our extensive experiments on a heart attack dataset demonstrate that C2SecFL provides a practical, auditable, and resilient approach to secure healthcare FL.