Advancing Federated Deep Learning Framework for Threat Prediction in Bio-Digital Systems

Rapid integration of digital and biological systems has revolutionized tailored treatment, human-computer interaction, and instantaneous medical diagnostics. Especially in neural interfaces, genetic data integrity, and distributed biodigital ecosystems, this junction raises serious security issues. This work presents a novel federated deep learning (FDL) system that specifically blends transformer-based topologies with quantum-inspired encryption to improve security, privacy, and scalability. Unlike conventional federated models, the proposed method dynamically protects bio-digital systems by including multi-modal threat intelligence, hence preserving data privacy in dispersed learning environments. By combining lattice-based encryption with federated learning and transformer-driven multi-modal analysis, this system is novel in enabling real-time, adaptive threat prediction. Comparative evaluations indicate amazing improvements in detection accuracy, processing efficiency, and system robustness, therefore validating the excellence of this paradigm over present methods. Positioned as a top option for safeguarding bio-digital ecosystems, maintaining neural interface security, and securing cyber-physical applications, the proposed architecture guarantees Future studies will focus on enhancing dataset variety, including realtime biometric threat detection, and boosting computational efficiency for edge deployments, so ensuring application across many security-critical sectors.