Spider-Tailed Bitterling Optimisation enabled key generation for secure data storage for Internet of Medical Things in federated learning

While the presented setting involves decentralised training of an ML model for privacy-preserving analytics in healthcare, the coupling of the two has also started to usher in significant security loopholes, predominantly relating to secure model aggregation while keeping key management efficient for resource-constrained IoMT devices. Hence, this paper aims to implement a hybrid bio-inspired optimisation method for key generation in a novel secure data storage framework. At the heart of the solution is the STBFO algorithm, which combines the best properties of two different natural-inspired techniques to generate cryptographic keys of very high entropy and strength. Hence, the architecture is designed to accommodate the Noise Parameter Server with the multi-phase key management scheme for sound privacy preservation. In testing the framework against real-life clinical datasets, both structured patient records and medical image data were used. The method brought about 17% less encryption time and 16.9% faster decryption compared to existing schemes. Having a 95.3% rate of privacy preservation and a 98.4% rate of success in authentication, our system is actually proven to be an efficiently secure anonymous user-preserving system for FL-enabled IoMT systems.