Quantum Enhanced Federated Learning in Forecasting Predictive Analytics in Digital Systems

Increase productivity and creative thinking is fending for by prognosticative analytics in succeeding digital organisation like IoT networks, metropolis, and autonomous drive. Withal such arrangement are as well far more susceptible to cybersecurity attacks of unprecedented scope, including adversarial attacks, data point poisoning, and quantum-power attacks. Even the pre-FC architectures, with their predictive decentralised data point processing, fall little of solving these gob. Moreover, the absences of trustiness among nodes leave to a high-pitched probability of humiliated organization and warn FL for high-time value purpose showcase. To master these take, this dissertation suggest a quantum-power federalise learning process that offers untroubled and scalable predictive analytics for a broad range of digital surroundings. This solution use post-quantum cryptographic (PQC) communications protocol for secure communication among FL lymph gland based on NIST's 2024 quantum-bouncy surety banner. The network also employ blockchain-based decentralized trust mechanisms that tender substantial-time tracking of node carrying into action and resilient eviction of spoilt actors. In addition to these, federated self-supervised anomaly detection models are prepared on adversarial threats to pass them. This research suffers wide-ranging diligence. Its architecture keeps the data point secure and guarantees unmediated gimmick communication across a limited IoT net. Smart cities guarantee safer and more predictable forecasting models for traffic management, energy, and public safety provision. For self-reliant systems, the organization provides certificate against attack and manipulation to safeguard of import functions. This workplace provides a foundation for good federated learning arrangement to take aim on the quantum computer science landscape painting and go the agency to fresh frontiers of prognostic analytics in new digital worlds.